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Understand How Large Language Models Work

By
Analytics Drift
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Understand How Large Language Models Work

Language is critical for something as simple as sharing ideas and thoughts to exploring complex concepts through structured words. With accelerating AI advancements, Large Language Models (LLMs) serve a similar purpose by serving as foundational algorithms for human-machine communication.

These models are engineered to understand and generate human language with contextual accuracy. By leveraging large datasets and advanced algorithms, LLMs make communication between machines and humans more efficient. These models promote innovation across diverse fields by enhancing efficiency and enabling new possibilities.

In this article, you will learn about the concept of LLM models, how they work, their practical applications, and future possibilities.

What Are LLMs?

Large Language Models (LLMs) are advanced AI systems programmed with techniques like deep learning to perform natural language processing (NLP) tasks. LLMs are capable of understanding context and generating coherent, contextually relevant responses. These systems use transformer models and are trained on large datasets, enabling them to recognize, translate, predict, and develop content, text, or other.

LLMs can analyze billions of parameters, enabling them to capture complex data patterns and perform a wide range of language-related tasks. These tasks include conversational responses, creative writing, and code generation. Some examples of LLM models include Open AI’s GPT-3, GPT-4, Google’s BERT, and Meta’s Llama Models.

How Does an LLM Model Work? 

LLMs are based on a unique structure called transformer architecture. This architecture allows an LLM to process data in two steps:

  • Tokenization: The first step involves breaking the input query into smaller units called tokens. These tokens represent either entire words or parts of words.
  • Correlation Analysis: In the second step, mathematical equations are applied to determine relationships between tokens. This helps the model recognize patterns that a human might find from the same input.

During the training stage, LLMs train from a large amount of text, which allows them to grasp the nuances of grammar, syntax, and context. Tokens are converted into numerical representations known as embeddings, which are processed by a transformer neural network.

The transformer uses attention mechanisms to focus on specific parts of the input data. These mechanisms enable the LLM model to capture complex relationships within the text, amplifying its understanding of context and meaning.

After you train the LLM model, it can predict the next word based on the received input and generate new text.

Why Are LLMs Becoming Important?

LLMs have been demonstrating their ability to generate meaningful output from minimal input. These models can adapt to various contexts, making them valuable for dynamic environments where language and communication styles shift rapidly.

The advancement of LLMs is transforming communication across industries by powering virtual assistants and automating content creation. LLMs are also refining how humans interact with technology, improving productivity and personalization in technology-driven environments for enhanced user experiences.

What Are Different Types of LLMs?

There are many different types of LLMs. Each model has a distinct architecture, training methods, and applications. Here are some of the primary types:

Autoregressive LLM Models

An autoregressive LLM model generates text by predicting the next word in a sequence, one word at a time, based on the preceding words. These models process text from left to right. This sequential approach enables the models to produce coherent and contextually accurate responses to input queries. 

One of the most prominent examples is Open AI’s GPT. It generates human-like text by predicting one token at a time. The GPT model uses natural language processing techniques to provide you with human-like responses. For example, you can enter a prompt asking GPT to write captions for your social media, and it will provide you with relevant content. 

Seq2Seq (Sequence-to-Sequence) Models

A Seq2Seq model is optimized for tasks that involve transforming one sequence into another, such as language translation, summarization, or question-answering.

These models use an encoder to process the input sequence and condense its meaning. Following this, a decoder helps generate the output sequence based on the encoded information. They can manage the context over entire sequences, making them suitable for applications requiring precise sequence transformation.

For example, Google Translate uses a Seq2Seq model to translate text between languages. The encoder processes the sentence in the source language and condenses its meaning. Then, the decoder uses the encoded information to produce coherent sentences in the target language.

BERT Models

Bidirectional Encoder Representations from Transformers (BERT) models are trained to understand the context by analyzing words before and after a given word in a sentence. This bidirectional approach allows the model to capture the nuances of language, making it practical for tasks requiring a deep contextual understanding, such as search engines. 

For example, in a search query like, “What are the benefits of exercise for mental health?” a BERT-based search engine interprets the relationships between words in the sentence. This makes the model understand the information the user seeks and then provides relevant search results.

Domain Specific Models

Domain-specific LLMs are trained on specialized datasets focused on particular fields such as healthcare, law, or finance. The targeted training enables the model to perform well in industry-specific applications by understanding terminology and context relevant to those domains.

An example of domain-specific LLM is ClinicalBERT, which is trained on clinical notes and electronic health records. This model enhances its understanding of medical terminology and patient care context. As a result, ClinicalBERT is useful in various healthcare applications, such as summarizing patient records and clinical decision-making.

Multimodal Models

Multimodal LLMs are advanced machine-learning models that are trained and fine-tuned on diverse datasets, including text, images, audio, and video. This provides AI with the capability to understand various sensory data types, offering you the flexibility to generate outputs in different formats.

For example, Google offers a multimodal model, Gemini, that can analyze an image of a dish and generate a detailed recipe as a response.

What Are LLMs Used For?

LLMs are becoming more versatile and can be applied across various tasks and industries. Here are some of the applications:

  • Text Generation: LLMs can generate human-like text. You can ask these models to write emails, blog posts, or other content forms by providing a prompt. They analyze and process the input prompt and provide the most appropriate outcome.
  • Summarization: You can utilize LLMs to summarize your extensive content. For example, you provide the model with lengthy articles, reports, or documents and instruct the model to summarize this data. The model can also identify and extract essential points from the text, providing you with structured insights to grasp key information quickly.
  • Code Generation: LLMs can be optimized for code-related tasks. These models can assist you in building applications, finding and debugging errors, and even translating code between different programming languages for better understanding.
  • Sentiment Analysis: Beyond simple keyword analysis, LLMs can help you understand the context and sentiment of a given text. For example, students can use LLMs to comprehend complex medical terms or historical texts. Businesses can also analyze customer feedback or social media posts to gauge sentiment and improve decision-making.

What Are the Advantages of LLMs?

  • Flexibility: LLMs are capable of performing various tasks, including answering questions, summarizing content, translating text, and generating code. You can use a single model for multiple applications to streamline processes and reduce complexity.
  • Extensibility: By fine-tuning your LLM model for task-specific purposes, it can meet your organization’s unique needs. For instance, you can train a model on medical terminology and clinical documents to assist doctors with charting or summarizing patient information.
  • Performance: LLMs deliver rapid responses to input queries. This ability makes them valuable for real-time applications such as virtual assistants, chatbots, and customer service platforms. Such quick response time enhances overall user experience and productivity.
  • Accuracy: LLMs are trained on extensive and diverse datasets. The training helps them deliver accurate results across various tasks, from generating content to analyzing complex information.

What Are the Limitations of LLMs?

  • Development and Operational Costs: LLMs require vast amounts of data for training, which can be resource-intensive to collect and process. There is also the need for specialized hardware like GPUs or TPUs for efficient computation, increasing the costs further.
  • Bias: LLMs are trained on large, unlabeled datasets gathered from diverse internet sources. These datasets may contain biases related to gender, race, etc., which the LLMs can inherit and reproduce. Bias removal during training isn’t always entirely effective.
  • Ethical Concerns: LLMs are associated with several ethical concerns. These concerns primarily include data privacy of sensitive information or using LLMs to generate unethical content like fake news or impersonations. Bias or insufficient training can result in the generation of incorrect or misleading information, requiring constant monitoring and fact-checking.

What is the Future of LLMs?

The popularity of models like ChatGPT, Claude 2, and Llama shows the increasing interest in AI systems that interact with users in a human-like manner. LLMs hold exciting possibilities in the future as efforts are underway to make these models more innovative and reliable.

Currently, most LLMs are trained primarily on text data. However, researchers and developers are now exploring ways to train these models using audio, video, and other multimedia inputs. Learning from such inputs can help LLMs understand context more comprehensively and recognize tones, emotions, and visual elements. This expands their applications in areas like multimedia content generation, interactive entertainment, and real-time contextual analysis.

Conclusion

LLMs are demonstrating a significant advancement in the field of AI. These models are transforming the way machines interact with humans. LLMs’ ability to understand and generate human language makes them resourceful across industries. Common applications include customer service, content generation, and data analysis.

As LLMs continue to evolve, they offer more flexible solutions for tasks such as text generation, summarization, translation, and sentiment analysis. 

FAQs

What is an example of an LLM? 

LLMs can be of various types. Examples include text generation models like GPT, domain-specific models like ClinicalBERT (for healthcare applications), and virtual assistants like Amazon Alexa.

Is BERT a Large Language Model?

Yes, BERT is a large language model. Developed by Google, it is widely used for tasks like text classification, question answering, and contextual understanding.

Are LLMs part of Generative AI?

Yes, LLMs are a subset of Generative AI. They specialize in generating human-like responses using natural language, making them a crucial part of many generative AI applications.

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AI in Decision Making: Everything You Need to Know

By
Analytics Drift
-
AI in Decision Making

Your business generates data from diverse sources like customer interactions, sales records, and marketing campaigns. With such massive datasets, it’s highly challenging to analyze all the information and make timely, informed decisions.  

Leveraging Artificial Intelligence (AI) in decision-making helps you overcome the complexities of handling extensive datasets. According to research, the global decision intelligence market was valued at USD 10.55 billion in 2022 and is projected to grow to USD 45.15 billion by 2032. Integrating AI into decision-making processes enables you to streamline data analysis, identify patterns, and derive actionable insights quickly and accurately.

What Is AI-Driven Decision Making?

AI-driven decision-making is the process of using artificial intelligence systems to analyze vast amounts of datasets and generate insights that help you make smart decisions. These systems leverage various advanced algorithms and machine learning to identify various patterns and predict possible outcomes accurately.

Typically, the process includes important steps like data collection, data processing, data analysis, and making predictions. Through these operations, the AI system can produce recommendations, determine optimal courses of action, or even take actions autonomously.

Importance of AI in Decision-Making

AI decision-making brings in several benefits. Here are a few of them:

Enhanced Efficiency

You can experience enhanced productivity as AI systems can process and analyze huge amounts of data. This enables you to make informed decisions more quickly, which helps you respond to changing market conditions or customer needs with greater agility.

Improved Accuracy

Using AI, you can minimize human error that may be caused by oversight. AI solutions process information consistently, so decisions based on those inputs are highly reliable. This helps you avoid costly mistakes and fosters trust in the outcomes. 

Cost Savings

AI reduces costs by automating repetitive tasks and simplifying processes. It eliminates the need for manual data analysis and redundant workflows, facilitating better use of resources. For example, AI chatbots can handle a large volume of customer inquiries, reducing the burden on support teams. 

Improved Risk Management

AI solutions enhance your ability to predict future outcomes with precision. By analyzing historical data and correlations, these systems forecast potential risks, enabling you to make informed decisions. For instance, in the finance sector, AI can anticipate stock market trends or evaluate credit risks. This lets you approach investments and lending decisions with increased confidence.

How Does AI Make Decisions?

The AI-driven decision-making process involves various steps. Let’s look at them in detail:

Data Collection

The performance of the AI models directly depends on the quality and quantity of the data. This data can come from various sources, such as databases, online sources, sensors, and more. Once the data is collected, you must preprocess it to remove any errors, inconsistencies, or redundancies.

Model Training and Testing

Model training is the process of feeding the preprocessed data into the AI model to learn the underlying patterns and relationships and produce accurate responses. Once the model is trained, you should test it on a separate dataset to evaluate its performance. This helps you identify any biases in the model and gives you an estimate of how the model will perform on new data. If the model’s performance is not satisfactory, you must retrain it with different parameters or more data.

Decision Making

Once the model is evaluated, you can use it to make data-driven decisions. The decision could be a prediction, a classification, or a recommendation based on the context. However, the responses generated by AI highly depend on the quality of the data, the appropriateness of the AI model, and the effectiveness of the training and testing processes. 

AI Decision-Making Examples

AI can be applied across various domains to support the decision-making process. Here are some examples:

Healthcare

AI-powered decision support systems like IBM Watson aid healthcare professionals in making accurate diagnoses. Through cognitive capabilities, it analyzes patient data, including lab results and medical records, and suggests tailored treatment plans. For example, Watson for Oncology helps oncologists to provide evidence-based treatment options personalized to each patient so they can have effective and targeted cancer care.

Marketing

Customer segmentation is a key pillar of targeted marketing. It involves dividing customers into categories based on criteria like purchasing behavior, preferences, and demographics. AI algorithms examine diverse datasets to identify patterns and correlations so marketers can make informed decisions. These insights enable them to create personalized marketing campaigns for each segment that drive customer engagement and conversions.

Financial Services

A survey revealed that more than 40% of financial institutions report a rise in fraud incidents, and 70% use AI and ML to mitigate such activities. AI-powered fraud detection systems can detect repeated fraud patterns and prevent large-scale attacks. For example, MuleHunter.ai, an artificial intelligence-based innovative model developed by RBI (Reserve Bank of India), facilitates real-time monitoring of transactions. This empowers banks to identify and respond to suspicious activities promptly.

Retail

AI plays a key role in optimizing stock management. By analyzing extensive data sets, including historical sales data and external factors such as holidays, AI solutions find trends in consumer behavior and predict future demand. This helps retailers to fine-tune inventory levels and ensure optimal stock availability, reducing the risk of overstocking or stockouts.

Challenges of Using AI in Decision-Making

Let’s explore the key challenges you may face when using AI in decision-making and possible solutions to overcome them.

Bias and Discrimination

Biases present in the training dataset can result in unfair outcomes. If the data majorly represents only specific groups or carries historical biases, then the same would be reflected in the decisions made by the AI model.

For example, Amazon faced a significant challenge with its AI recruiting tool, as the algorithm was found to be biased against women. This happened because the algorithm was trained on data submitted by applicants over a decade. Since most of the applicants were men, it was trained to favor men over women.

Solution: To mitigate the risk of bias, you must ensure that the training data contains a wide range of scenarios and demographic groups. Regularly audit the data and model outputs to identify and address any potential biases. You can use tools like IBM AI Fairness 360 or Microsoft’s Fairlearn to assess and improve the reliability of your AI systems.

Data Quality Issues

AI algorithm’s performance relies highly on training data. High-quality data sets enable AI models to recognize patterns and make accurate predictions. However, inaccurate or incomplete data mislead AI systems and lead to operational inefficiencies and poor decision-making.

Solution: Use data quality tools to automate the process of data validation, cleansing, and transformation. These tools help you detect inconsistencies, missing values, duplicates, and outliers, ensuring that only accurate data is fed into AI systems.

Lack of Transparency

AI transparency helps you access information to understand how an artificial intelligence system makes decisions. However, most current AI models are closed and operate as black boxes. You cannot know the data sources and processes are not traceable. This can lead to disparities in how AI models are developed, deployed, and interpreted, impacting their transparency and trustworthiness.

Solution: You can achieve AI transparency through proper disclosure. Document and share the underlying AI algorithm’s logic, the data inputs used to train the model, the methods used for model evaluation, and more. You should focus on designing AI models that are explainable and interpretable. This results in ethical AI usage and fosters trust in your AI applications.

Accountability Issues

Accountability issues arise when AI systems make decisions that negatively impact individuals or groups. Determining who is responsible for the outcomes of AI-driven decisions can be complex. For example, suppose an AI system denies a loan application based on biased algorithms. In that case, it may be unclear whether the responsibility lies with the developers or the organization using that AI application.

Solution: Define who is responsible for the actions of an AI system. This includes the developers, operators, and organizations deploying the AI. Assigning accountability ensures that there are designated individuals or entities who can be held accountable for any negative consequences. Further, maintain thorough documentation of the AI system’s decision-making processes for review and reporting purposes to ensure accountability.

Privacy Concerns

The data sources used by the AI models might contain personal information. The collection, storage, and processing of such sensitive information can lead to breaches of privacy if not handled securely. For example, AI solutions in customer service or targeted ads often analyze personal data without clear consent. This can cause violation of privacy regulations like GDPR.

Solution: You can address privacy concerns by prioritizing user consent and data anonymization. Use methods like differential privacy and homomorphic encryption to protect sensitive information. Further, adhering to data protection regulations such as CCPA and GDPR ensures compliance and fosters public trust.

Final Thoughts

The incorporation of AI in decision-making represents a transformative shift in how you approach problem-solving. It empowers you to leverage data-driven insights, streamline processes, and make more informed choices. This article highlighted the importance of AI-assisted decision-making, including the benefits and real-world examples. Further, it presented the major challenges you face in automated decision-making, along with possible solutions to overcome them.

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Top 7 LLMs of 2025: A Comprehensive Guide

By
Analytics Drift
-
Top 7 LLMs

Large Language Models (LLMs) are more influential in transforming the future of AI. These complex systems are built to understand and generate more natural, human-like text, enabling efficient interaction between people and machines. Their applications are impactful in several industries, including healthcare, finance, education, and entertainment. From answering questions to crafting creative stories, LLMs are changing how we engage with technology in our daily lives.

While many LLMs are available in 2025, you might not know the best of them. This comprehensive guide will introduce you to the top 7 LLMs of the year, allowing you to explore each model’s unique capabilities and features.

Let’s get started!  

What Is an LLM?

An LLM is a type of artificial intelligence (AI) model designed to understand, generate, and process human language. These models are built and trained on large amounts of data. During the training process, LLMs learn the complexities of the language, the relationships between words, and the intended messages behind sentences. 

When you provide LLM with a prompt, it generates a response by predicting the next text segment based on the input. Unlike traditional systems that search for keywords to give standard responses, LLMs strive to understand the meaning of your request and provide relevant answers. This ability makes LLM so popular, driving their use in various applications, including AI chatbots, AI virtual assistants, and AI writing generators. 

Benefits of Using LLMs

  • Enhanced Efficiency: LLMs can process and analyze vast amounts of data rapidly. This reduces the time required for various tasks, such as document summarization, content generation, and customer query handling. 
  • Self-Attention Mechanism: One of the key innovations in LLMs is the capability of the attention mechanism. It enables the model to weigh the importance of different words of the input text and their relationships in context. 
  • Scalability: With the ability to process high volumes of queries simultaneously, LLMs are suitable for scenarios with high customer interaction demands.
  • Personalization: You can fine-tune LLMs on specific datasets to cater to particular business or user preferences.
  • 24/7 Availability: LLMs can operate continuously without breaks, providing full-time assistance to users.
  • Language Translation: Many LLMs can understand and respond in multiple languages to enable smooth cross-cultural interaction. 

How Do LLMs work?

LLMs work through a combination of advanced machine learning and NLP techniques to process and generate human language. Let’s see how it operates:

Data Collection

LLMs rely on extensive datasets from diverse sources, such as books, articles, websites, and social media. These datasets help the model learn about language patterns, grammar, contexts, and semantic meaning.

Data Processing

Before training, the raw data must undergo several processing steps, including cleaning, standardization, and filtering, to remove irrelevant or low-quality text. Following this, the text is broken down into smaller units called tokens. These tokens can be individual words, subwords, or characters and enable the model to manage and analyze the text efficiently. 

Once tokenized, the individual tokens can then be represented as numerical vectors in high-dimensional space, known as vector embeddings. Words with similar meanings are placed closer together in this space, enabling the model to understand semantic similarities. 

Transformers Architecture

Transformers are deep neural network architecture behind LLMs. It consists of multiple layers of neurons, where each layer refines its understanding of the input text. These transformers’ self-attention mechanism enables context-aware understanding. When you train the LLM on massive amounts of data using transformers, the model can predict the next word in a sequence based on preceding words. 

Fine-Tuning

After initial training, LLMs can undergo fine-tuning using smaller or task-specific datasets. This process enhances their performance in certain areas, such as translation, summarization, or sentiment analysis. During fine-tuning, the model adjusts its hyperparameters, like weights and biases in its neural layers, based on the new data. These adjustments gradually improve the prediction accuracy for the specific task. 

Seven Top LLMs 2025

Out of many LLMs available in the market, here’s a closer look at the best 7 LLMs you should consider exploring:

GPT

GPT, or Generative Pretrained Transformer, is a series of general-purpose LLMs developed by OpenAI for generative AI. It uses a transformed-based deep learning architecture to process and generate human-like text. The first version, GPT-1, introduced in 2018, is a 12-layer decoder-only model, with each having masked self-attention heads to represent a variety of linguistic features. Despite its relatively small size with 117 million parameters, GPT-1 can carry out zero-shot performance on various tasks like text completion, summarization, and basic Q&A. 

Following GPT-1, OpenAI released GPT-2 in 2019, which features a much larger architecture with 48 decoder layers and 1.5 billion parameters. GPT-2 performs well in identifying long-range dependencies, which are relationships between words and phrases that are far apart in a sentence or paragraph. It is also good at completing a sentence based on the preceding context. 

For advancements, GPT-3, with 96 decoded layers and 175 billion parameters, was launched in 2020. This model is capable of solving arithmetic problems, writing code snippets, and executing intelligent tasks. Its successor, GPT-3.5, enhanced its abilities to understand context and maintain coherent conversations, making it more effective for dialogue-based applications. 

With the launch of GPT-4, the model has the ability to process both text and images by utilizing reinforcement learning for refined outputs. In 2024, OpenAI launched its high-intelligence language model GPT-4o for multi-step problem-solving tasks, which is much faster and cheaper than the GPT-4 architecture. 

On December 21st, 2024, the platform announced its o3 series to tackle advanced reasoning tasks. However, these models are undergoing testing, with early access available only to safety and security researchers, and are expected to be publicly released in 2025.

Gemini

Gemini is Google’s largest and most capable AI model. It is designed to process text, images, and audio data simultaneously. The Gemini model’s advanced multimodal reasoning capabilities enable it to analyze complex written and visual information. The model can also help interpret and generate high-quality code across popular programming languages like Java, Python, C++, and Go. 

The first version of Gemini 1.0 has been optimized for three different sizes: Ultra, Pro, and Nano. Ultra is the Gemini’s most advanced model for performing highly complex tasks. This model has outperformed 30 out of 32 leading academic benchmarks, including MMLU, Big-Bench Hard, DROP, MATH, HumanEval, Natural2Code, and more. Ultra notably scored 90% on the MMLU benchmark, surpassing human experts. It also achieved the highest percentage on the MMMU benchmark for handling multimodal reasoning. 

Before developing the Ultra model, Google launched Gemini Pro to scale across various tasks. Using Gemini Pro, Google introduced a more advanced code generation solution, AlphaCode 2, for solving programming challenges. 

Claude

Claude is an LLM developed by Anthropic. It is trained to be a helpful and harmless AI assistant. While prioritizing safety, Claude engages users in natural, conversational interactions. 

Claude possesses several capabilities, including advanced reasoning, which enables the model to deal with complex cognitive tasks. It can also transcribe and process various static images, ranging from handwritten notes and graphs to photographs. Additionally, Claude enables you to write code, create websites in HTML and CSS, convert images into structured JSON data, and debug complex codebases. 

Apart from these capabilities, Claude features three different models—Haiku, Sonnect, and Opus—tailored to speed and performance. Haiku is the fastest Claude model, allowing you to run lightweight tasks with high speed. Conversely, Sonnet balances performance and speed, making it excellent for high-throughput operations. On the other hand, the most powerful model, Opus, can handle complex analysis and long math as well as coding challenges. 

LLaMA

LLaMA (Large Language Model Meta AI), developed by Meta in 2023, is a family of open and efficient foundation language models to advance conversational AI. These models are trained on trillions of tokens in publicly available datasets and range in size from 7 billion to 65 billion parameters. Its 13B parameter model, LLaMA-13B, outperforms 175B GPT-3 on most NLP benchmarks. However, LLaMA had fewer parameters, which sometimes made it struggle with precise text understanding and provide inconsistent responses. 

Meta then launched Llama 2, a set of pre-trained and fine-tuned LLM that are trained on 2 trillion text tokens to better understand the language. Llama 2 could read longer text passages with a doubled context window of 4,096 tokens to decrease inconsistencies. Despite these improvements, LLama 2 still needed more computing power, enabling Meta to focus on developing LLama 3.  

Llama 3 released four versions, including 8b, 8b instruct, 70b, and 70b instruct. These models are trained on 15 trillion tokens, and over 5% of that training data can be represented in 30 different languages. All versions can run on different types of devices and handle longer passages with an 8K token limit. 

Gemma

Gemma is a set of lightweight, text-to-text, and decoder-only LLMs. It is trained on a vast dataset of text, code, and math content using the Tensor Processing Unit (TPU) hardware and Google’s ML Pathways with JAX. Gemma was developed by Google DeepMind in mid-2024 using the same research and technology behind Google’s Gemini models. 

The initial Gemma release comes in 2B and 7B parameter sizes. Both versions are available to run in your applications and on your hardware. You can also customize the behavior of the models with additional training to perform specific tasks. 

To support different needs, Gemma models are available in instruction-tuned (IT) and pre-trained (PT). The IT models are fine-tuned with human conversations to respond to user input, like a chatbot. In contrast, PT models are trained only on the Gemma core dataset and lack specific task instructions. For the best results, you must fine-tune the PT models before they are deployed into applications.  

Following this, DeepMind released CodeGemma, RecurrentGema, and PaliGemma models for coding, memory-efficient tasks, and advanced image processing, respectively. With the release of Gemma 2 PT models, improved performance has been shown in natural language understanding and reasoning tasks across various benchmarks. These models are optimized in three parameter sizes—2B, 9B, and 27B. The team reported that the 2B Gemma 2 version outperforms all GPT-3.5 models on the LMSYS Chatbot Arena Leaderboard.

Command R

Command R, introduced by Cohere in 2024, is a series of highly scalable LLMs with top-tier performance. It is paired with Cohere Embed, a multimodal embedding model, and Rerank, a tool to improve search quality. This combination provides strong accuracy for advanced AI applications that need data from documents and enterprise sources. 

One of the major abilities of Command is that it allows you to develop applications that speak fluently to the business world in 10 different languages. Based on BLEU, a popular machine translation quality metric, Command R has better quality than Claude Sonnet and GPT-4 Turbo. This conclusion is drawn from evaluations conducted using two test sets of Flores and WMT23.  

With the release of Command R+, you can deliver safe and reliable enterprise-grade solutions, as it is optimized for advanced Retrieval-Augment Generation (RAG). This new model enhances the accuracy of responses and offers in-line citations to effectively reduce hallucinations. It also helps upgrade AI applications, transforming simple chatbots into powerful, robust AI agents and productive research-oriented tools. 

Falcon

Falcon is a generative LLM launched by the UAE’s Technology Innovation Institute (TII). The initial version, Falcon-40B, is a foundation language model with 40 billion parameters and was trained on a trillion tokens.

The Falcon-40B version features a decoder-only architecture, which is optimized for high-speed inference through FlashAttention and multi-query. FlashAttention is a memory-efficient technique that accelerates attention calculations, allowing the model to focus on relevant patterns more quickly without affecting accuracy. Contrarily, multi-query enables faster processing by having a single attention head shared across multiple queries. This reduces the memory needed to handle each query individually.  

Alongside, you can explore Falcon 180B, a 180 billion parameter-sized casual decoder-only model trained on 3,500 billion tokens. With a high parameter count and optimized infrastructure, Falcon-180B can handle large-scale, resource-intensive enterprise applications. If you are looking for smaller and less expensive models, the Falcon-7B is the best, and it is trained on 1500 tokens.  

Selecting the Right Open-Source LLM for Your Needs

Choosing the appropriate open-source LLM depends on your specific requirements. Consider the following factors:

  • Model Size: Select a model that aligns with your computational resources and performance needs. Smaller models like LLaMa-2B are more efficient for environments with limited resources. On the other hand, larger models like LLaMA-7B or GPT variants are excellent for handling complex operations with high accuracy.
  • Task Suitability: Different models are optimized for different tasks. Ensure the model is ideal for your use case, whether it’s chatbots, text generation, or specialized research applications.
  • Customization: Some open-source models allow fine-tuning and further customization to fit your unique needs, such as industry-specific terminology or proprietary datasets.
  • Community Support: Opt for models with strong community support and active development, which can provide updates, bug fixes, and additional resources.

Use Cases of LLM Models

  • Healthcare: LLMs are helpful for automating patient inquiries, generating medical reports, assisting in diagnostics, and scheduling doctor appointments. Studies have shown that AI models, including LLMs, can reduce the time spent on administrative tasks.
  • Multimodal Document Understanding: A team at JP Morgan has launched DocLLM, a layout-aware generative language model for multimodal document understanding. This model uses bounding box information to process the spatial arrangement of elements in the documents. 

Conclusion

LLMs are transformative AI solutions that enhance tasks such as customer service, content generation, and coding assistance across various industries. However, you must be aware of their limitations, including the potential for inaccuracies and privacy concerns. By leveraging LLMs responsibly and understanding their capabilities, your organizations can maximize the benefits while minimizing risks.  

FAQs 

Are LLMs safe to use?

Most LLMs include robust safety measures to minimize harmful outputs. However, you should remain cautious about relying entirely on LLMs for critical decisions. 

Can LLMs be fine-tuned for specific applications?

Yes, you can fine-tune the LLM with additional training on specialized datasets to improve their performance in targeted applications. 

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A Comprehensive Guide to Data Virtualization

By
Analytics Drift
-
Data Virtualization

For many businesses, including yours, the complexity of accessing and integrating data from various systems and formats can be a major challenge. Data virtualization offers a smart solution, simplifying data management and facilitating easy access to data from diverse sources without the need to move or copy it. According to Allied market research, the data virtualization market was worth $3.2 billion in 2021 and is expected to grow to $22.2 billion by 2031. 

Data virtualization technology plays a crucial role in streamlining business operations and facilitating efficient real-time decision-making. If you’re looking to understand how this technology can be applied to your business, you’re in the right place! 

What Is Data Virtualization?

Data virtualization is an advanced data integration technology that acts as middleware between different data sources and end-users. It allows you to retrieve and manipulate data from multiple sources without physically moving it into a single repository, such as a data warehouse. 

By creating a virtual layer, the virtualization technology enables you to seamlessly integrate structured and unstructured data stored in different systems, databases, formats, and locations. You can then access the integrated data for various use cases, including enterprise systems, reporting, Business Intelligence (BI), or mobile and web applications.  

Key Capabilities of Data Virtualization

  • Unified Data View: Data virtualization allows you to combine data from multiple sources into a single virtual view of data. This helps you access and work with data without physically dealing with different systems.
  • Real-Time Access: You can retrieve and process data on demand, providing up-to-date information whenever required. As a result, you can make decisions based on the latest data.
  • No Data Replication: Leveraging data virtualization technology can help you reduce storage costs by reducing the need to copy or move data.
  • Data Abstraction: You can hide the complexity of underlying data sources by providing a simple unified interface through data virtualization. It makes accessing and using data easier, even from different systems.  
  • Efficient Data Management: Data virtualization provides a secure, centralized layer to help you search, discover, and govern the available data. You can also explore hidden relationships between these datasets. 
  • Agile Development: Virtualized data systems allow you to quickly create and modify data views according to changing business needs. This agility speeds up project development and improves the time to solution.
  • Analyze Business Performance: You can analyze your organization’s performance by comparing current and historical data from previous years. This will help you understand and plan for future improvements.

Importance of Data Virtualization in Cloud Computing

Data virtualization is crucial in cloud computing as it helps you simplify the integration of data stored across various cloud platforms. The virtual abstraction layer provides a unified view of data, eliminating the need for physically moving or replicating data. It also reduces storage costs and the complexity of managing multiple data sources in the cloud. 

With data virtualization technology, you can enhance the scalability and flexibility of cloud environments. As your cloud infrastructure expands, data virtualization enables you to handle increasing data volumes without requiring significant changes to the system. It also strengthens data security by centralizing access control, ensuring that sensitive data is secured and compliance policies are upheld. 

Top 3 Data Virtualization Tools

There are several data virtualization tools to help you manage your data efficiently. Let’s discuss the top three tools among them:

Denodo

Denodo is a leading logical data management platform that supports data virtualization. It allows your application to utilize data from several heterogeneous data sources. You can access and integrate data in real-time between different distributed systems without copying or moving data from its source.   

The Denodo platform includes the following components:

  • Virtual DataPort: This module allows you to create virtual views that help you combine data from different systems. It provides a JDBC/ODBC driver and SOAP/REST web services to allow you to query these views. 
  • Aracne: It enables you to crawl and index unstructured data from websites, email servers, file systems, and so on.
  • ITPilot: You can access, structure, and query data on the Web using ITPilot.
  • Scheduler: Scheduler allows you to schedule jobs to connect with the other modules of the Denodo platform. 

TIBCO Data Virtualization

TIBCO Data Virtualization is a data virtualization system provided by TIBCO software. It allows you to aggregate disparate data sources on demand. Using the software, you can create logical, unified data views tailored to your analytical requirements. With support for JDBC, ODBC, REST, and SOAP, TIBCO Data Virtualization helps you connect to virtually any data source. 

The TIBCO Data Virtualization (TDV) has the following modules to support all phases of data virtualization development, run-time, and management:

  • Studio: An agile modeling, development, and resource management tool that helps you model, develop, and view data services. It also allows you to build custom transformations, optimize queries, and handle resources.  
  • Web UI: A browser-based interface includes a data catalog and data workbench for self-service data discovery. It facilitates efficient data integration and collaboration by enabling you to visualize, access, and manage virtualized data in real-time.  
  • Adapters: A module that offers various data source connectivity for databases, big data, cloud services, applications, etc. You can also build custom adapters using the Data Source Tool Kit.
  • Cost-based and Rule-based optimizers: These optimizers are used to improve query performance.
  • Manager: An administrative console that enables you to configure the user IDs, passwords, security profiles, and more.
  • Deployment Manager: This module allows you to move all the projects across various instances in one go quickly.
  • Monitor: You can access a detailed, real-time view of your TDV cluster, which will help you take corrective actions based on the performance indicators.   
  • Active Cluster: It works in association with load balancers to offer high scalability and availability. 
  • Business Directory: A self-service directory offers a list of published resources involved in one or more instances of TDV. 

CData Virtuality

CData Virtuality is an enterprise data virtualization platform offered by CData Software. It is designed to meet increasing business demands by offering agile, scalable, and efficient data integration methods. This solution is suited for modern data challenges, including AI initiatives, flexible data architectures, and self-service analytics. Focusing on cloud-native readiness and minimizing physical data movement helps you ensure optimal performance and adaptability. 

There are four key pillars for modern data management using CData Virtuality:

  • Seamless Integration: Bridging the gap between modern digital and traditional systems, CData Virtuality facilitates real-time insights by enabling you to connect heterogeneous data sources. This ensures unified access to data, regardless of its location or format. 
  • Effortless Data Preparation: By integrating virtual and physical data models, the platform allows you to accelerate data preparation processes without scalability limitations. 
  • Robust Governance: CData Virtuality provides centralized governance by managing both physical and virtual data assets with related business, operational, and technical metadata.  
  • Accelerate Data Delivery: CData Virtuality makes it easy for you to deliver data across different environments, ensuring it reaches the suitable users at the right time. 

Data Virtualization Benefits

  • Time-to-Market Acceleration: Many data virtualization tools offer pre-built connectors, templates, and wizards that streamline deployment. This reduces the time and expertise required to integrate sources. 
  • Support for Modern Architectures: Modern data virtualization platforms align with data mesh and data fabric architectures. It supports distributed environments while maintaining a centralized, governed data layer. 
  • Improved Customer Satisfaction: By delivering faster insights and a comprehensive view of customer data, data virtualization helps you improve customer experience. Personalized services, faster response times, and better support result in higher satisfaction, which increases customer loyalty and drives revenue growth. 
  • Robust Security Mechanisms: Within data virtualization platforms, you can incorporate advanced security measures such as encryption, role-based access control, and audit trails. These mechanisms enable you to protect your sensitive personal and professional information even if it is accessed across multiple systems. 
  • Creation of Personalized Views: Data virtualization solutions include intuitive interfaces that allow you to create customized views of the data. These personalized views simplify complex datasets, allowing you to focus on insights rather than data preparation. 
  • Cost Efficiency: Traditional data integration methods require duplicating data across various systems, which increases storage and infrastructure costs. Using data virtualization, you can reduce this by creating virtual views of the data, enabling data access without physical replication. 

Limitations of Data Virtualization

  • Single Point of Failure: Data virtualization relies on a central server to provide connectivity to various sources, creating a single point of failure. If the virtualization server experiences downtime, it can disrupt access to all connected data sources, significantly affecting operations.  
  • Scalability Constraints: As the number of data sources and the volume of data increases, maintaining real-time access through virtualization becomes increasingly demanding. Scaling the infrastructure to handle these workloads efficiently can be costly and technically challenging. 
  • Limited Offline Support: Data virtualization systems usually do not store data permanently. This limitation makes them unsuitable for offline analysis, as all queries depend on live connections to the sources. 

Use Cases of Data Virtualization

Some key areas where you can utilize data virtualization: 

Real-time Analytics

Data virtualization provides a consolidated view of data from various sources, enabling real-time insights. Your business can access and process up-to-date data to improve decision-making speed and efficiency. 

Hedge funds are investment firms that utilize data virtualization to integrate and analyze live market data, stock prices, and social media streams. It allows them to make informed and prompt investment choices. 

Coforge, an IT services company that offers end-to-end software solutions, utilizes a data virtualization framework. This framework supports data analytics by enabling smooth access and control over data spread across several databases and systems.   

360-Degree Customer View 

A 360-degree view of customer information enables you to identify key attributes such as customer profiles, behavior, and demographics. Data virtualization has a significant role in creating this holistic view by integrating disparate data sources. 

Retailers depend on data virtualization to aggregate information from systems such as point-of-sale, e-commerce, and loyalty programs and generate a 360-degree customer view. 

Healthcare Industry

Healthcare operates under strict regulations, such as HIPAA, which mandates patient data security and proper management. Data virtualization enables healthcare providers to combine data from electronic medical records, insurance claims, and other sources into a single view. 

Conclusion

Data virtualization transforms how your business accesses and utilizes data, enabling streamlined operations, cost efficiency, and real-time analytics. While challenges exist in scalability and dependency on live connections, the benefits often outweigh these limitations. By choosing the right virtualization tools and strategies, your business can leverage the full capabilities of data virtualization, improving productivity. 

FAQS

What makes data virtualization different from ETL?

ETL (Extract, Transform, Load) enables you to move data into a central repository. In contrast, data virtualization creates a virtual layer that allows you to access data without physical data movement. This ensures faster insights and reduced storage requirements. 

Can data virtualization be used with big data technologies?

Yes, data virtualization can integrate with big data technologies like Hadoop, Spark, and NoSQL databases. 

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Quantum Machine Learning: The Next Frontier in Redefining AI

By
Analytics Drift
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Quantum Machine Learning

Building and deploying conventional machine learning (ML) models has become challenging due to the increasing volume and complexity of data. These models can sometimes perform inefficiently or generate inaccurate results. A suitable solution to overcome these limitations is quantum machine learning.

By utilizing quantum computing technology, quantum ML allows you to refine the functionality of classical ML algorithms, offering enhanced performance and prediction accuracy. Quantum ML is also valuable for critical tasks such as developing new materials, drug discovery, and natural language translation.

To build quantum ML models for your specific use cases, you must understand what quantum machine learning is, its advantages, and implementation challenges. Let’s get started!

What Is Quantum Machine Learning?

Quantum machine learning (QML) is a technology that integrates quantum computing with machine learning to generate results that outperform conventional ML models. The field of quantum computing involves the use of quantum mechanics to help you solve complex problems quickly.

Quantum computing finds its use in devices like quantum computers to facilitate faster computational operations. Unlike classical computers that store data in binary bits, quantum computers use qubits, the quantum equivalent of binary bits. In binary form, data can exist either in 0 or 1 state, while as a qubit, data can exist in multiple states in addition to 0 and 1. This unique property gives quantum computers an exceptionally high storage capacity and processing power.

By combining the advanced capabilities of quantum computing with machine learning, you can build quantum ML models that produce highly accurate outcomes in minimal time.

Why There Is a Requirement for Quantum Machine Learning?

There are some challenges associated with classical machine learning models. Some of the reasons that make classical machine learning models inefficient include:

  • As the dimensions of training data increase, classical ML models require more computational power to process such datasets.
  • Despite parallel processing techniques and advancements in hardware technologies like GPUs and TPUs, classical ML systems have scalability limits. Due to these constraints, you cannot significantly enhance the performance of such ML models.
  • Classical ML models cannot process quantum data directly, which is useful for solving complex scientific problems. Converting quantum data into a classical format can lead to data loss, reducing the accuracy of the models.

Quantum machine learning can help address these limitations. You can train quantum ML models directly on large volumes of quantum data without loss of information. These models can also be trained on high-dimensional datasets because of quantum mechanical phenomena like superposition and entanglement. Let’s learn about these mechanisms in detail in the next section.

Quantum Mechanical Processes That Help Improve Machine Learning Efficiency

Quantum computing relies on multiple processes that help overcome the limitations of classical machine learning. Let’s look into these processes in detail.

Superposition

Superposition is a principle of quantum mechanics where a quantum system can exist in multiple states simultaneously. This capability allows you to represent high-dimensional data compactly, reducing the use of computational resources.

With superposition, you can also execute several operations in quantum ML models at the same time. This reduces computation time for tasks such as pattern recognition and optimization.

Entanglement

Quantum entanglement is a phenomenon that takes place when the quantum states of two or more systems become correlated, even if they are separated spatially. In Quantum ML, entangled qubits can represent strongly interrelated data features, which helps ML models identify patterns and relationships more effectively.

You can utilize such entangled qubits while training ML models for image recognition and natural language processing tasks.

Interference

Interference occurs when quantum systems in a superposition state interact, leading to constructive or destructive effects.

To better understand this concept, let’s consider an example of classical interference. When you drop a stone in a pond, ripples or waves are created. At certain points, two or more waves superpose to form crests or high-amplitude waves, which is called constructive interference. On the other hand, destructive interference arises when waves cancel each other out.

In quantum ML, you can utilize interference in Quantum Support Vector Machines (QSVM) to streamline pattern recognition and improve the accuracy of classification tasks. QSVM are supervised learning algorithms that help with classification and regression learning techniques.

Advantages of Quantum Machine Learning

After understanding the processes contributing to quantum ML’s efficiency, it is evident that this technology has numerous benefits. Here are a few advantages of using quantum ML:

Enhanced Speed of ML Models

Quantum computing helps significantly accelerate the performance of ML models through qubits and quantum mechanical processes. It simplifies handling large datasets with numerous features, facilitating their use for model training with minimum computational resources. As a result, quantum ML models are high-performing and resource-efficient.

Recognizing Complex Data Patterns

Some datasets, such as those related to financial analysis or image classification, are complex. Conventional ML models may find it difficult to identify patterns and trends in such datasets. However, quantum machine learning algorithms can help overcome this hurdle using the entanglement phenomenon. This offers superior predictive capabilities by recognizing intricate relationships within the datasets.

Enhanced Reinforcement Learning

Reinforcement learning is a machine learning technique that allows models to make decisions based on trial and error methods. These models refine themselves continuously depending on the feedback they receive while training. As quantum ML models are capable of advanced pattern recognition, they accelerate the learning process, enhancing reinforcement learning.

Challenges of Deploying Quantum ML Models

While quantum ML offers some remarkable advantages over classical ML models, it also has challenges that you should be aware of before implementing quantum ML. Some of these challenges include:

Decoherence

Decoherence is the phenomenon in which a quantum system loses its quantum properties and starts following principles of classical mechanics. Qubits are sensitive and can lose their coherence when disrupted by even slight noise or disturbances. Such diminishment of coherence can lead to information loss and inaccuracies in the model outcomes.

Ineffectiveness of QNN Models

Quantum neural network (QNN) models mimic the functionality of human neural systems. However, QNN models can be affected by the phenomenon of barren plateaus. It occurs when ML algorithms cannot produce the desired output due to the loss of gradients in the cost function related to quantum parameters. This issue can significantly hinder the training process, reducing the efficiency of QNN models.

Infrastructural Inaccessibility

The infrastructural requirements of quantum ML involve access to costly and high-maintenance quantum computers. Some cloud-based quantum computing platforms exist, but they are inadequate for robust training of complex ML models. You also need to invest in tools to prepare datasets used to train the quantum models, which further increases the implementation costs.

Lack of Technical Expertise

Quantum technology and machine learning processes are still in developmental stages. This makes it difficult to find skilled professionals who are experts in both these disciplines. To hire suitable candidates, you must offer substantial salaries, impacting the budget of other organizational operations.

Use Cases of Quantum Machine Learning

According to a report by Grand View Research, the quantum AI market size reached 256 million USD in 2023 and is expected to grow at a CAGR of 34.4% from 2024 to 2030. This shows that there will be extensive growth in the adoption of quantum AI and machine learning-based solutions.

Some of the sectors that can leverage quantum ML are:

Finance

Since quantum ML models produce highly accurate predictions, you can use them to analyze financial market data and optimize portfolio management. By leveraging quantum ML models, you can also identify suspicious monetary transactions to detect and prevent fraud.

Healthcare

You can utilize quantum ML models to process large datasets, such as records of chemical compounds, and analyze molecular interactions for faster drug discovery. Quantum ML models also assist in the recognition of patterns from genomic datasets to predict genetics-related diseases.

Marketing

Quantum ML models allow you to provide highly personalized recommendations to customers by assessing their behavior and purchase history. You can also use this information to create targeted advertising campaigns, resulting in improved customer engagement and enhanced ROI.

Conclusion

Quantum ML is a rapidly developing domain that has the potential to revolutionize the existing functionalities of machine learning and artificial intelligence. This article provides a comprehensive explanation of quantum machine learning and its advantages. The notable benefits include improvement in models’ performance speed and accuracy.

However, quantum ML models also present some limitations, such as decoherence and infrastructural complexities. Knowing these drawbacks makes you aware of potential deployment challenges. You can use this information to develop an effective quantum machine learning model that can make highly precise predictions.

FAQs

What is a qubit?

A qubit is a quantum mechanical counterpart of the classical binary bit. It is the basic unit of information in quantum computers. A qubit can exist in a state of 0, 1, or any superposed state between 0 and 1. This enables qubits to store more data than conventional binary bits.

What is quantum AI?

Quantum AI is a technology that utilizes artificial intelligence and quantum computing to perform human intelligence tasks. One of the most important components of quantum AI is the quantum neural network (QNN), a quantum machine learning algorithm. You can use quantum AI in fields such as finance and physical science research to recognize common patterns and solve advanced problems.

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Top 5 Cloud Service Providers in India

By
Analytics Drift
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Cloud Service Providers in India

India’s digital infrastructure is rapidly expanding, reshaping the operability of various industries. This surge in demand for digital services is prompting businesses to adopt cloud technology to stay competitive and meet customer needs. The article lists the top cloud service providers in India and showcases how the leading platforms drive digital transformation. 

What is a Cloud Service Provider?

A cloud service provider is a third party that delivers cloud-based solutions to you over the internet. The providers manage and maintain the underlying hardware, data centers, and software updates so you can access and scale resources without worrying about technical complexities. With the infrastructure handled externally, you can entirely focus on your goals. 

Types of Cloud Services

  • IaaS (Infrastructure as a Service) is a cloud service that offers on-demand access to virtual computing resources, such as servers, storage, and networking. Your organization can scale the resources up or down based on the workload, facilitating flexible and cost-effective data-driven operations.   
  • PaaS (Platform as a Service): PaaS provides a cloud environment with tools that help you build, test, and deploy applications. The developers in your organization can focus on coding and application management, as the cloud providers handle the underlying operating system, middleware, and infrastructure.
  • SaaS (Software as a Service): A software distribution service that allows you to access applications over the Internet. SaaS offers you a complete software solution that you can rent and use. Cloud service providers handle the underlying aspects of managing infrastructure, backups, and updates.

Top Cloud Providers in India 

As India’s digital transformation accelerates, the demand for reliable cloud solutions is at an all-time high. Here are some of the best cloud companies in India, each offering tools needed to innovate and modernize business operations:

Amazon Web Services 

Amazon Web Services (AWS) is one of India’s leading cloud service providers. It offers various cloud-based solutions, including services for computing, storage, databases, analytics, security, and IoT. These services are scalable, flexible, and drive innovation. AWS operates its cloud services from 34 geographical regions worldwide.

Key Features

  • Elasticity and Scalability: Amazon EC2 offers scalable computing capacity, which you can scale up or down according to your requirements. This flexibility helps you to handle fluctuating workloads. 
  • Data Storage: Amazon offers the Simple Storage Service (S3), a scalable solution that can be optimized for data storage, backup, and archiving. 
  • Data Analytics: There are various AWS tools like Redshift, Amazon EMR, QuickSight, and Athena that enable your businesses to process and analyze large datasets. For example, Amazon Redshift is a fully managed data warehouse that facilitates data analytics by running complex queries on your datasets.
  • Security: AWS offers various security features, including identity access management encryption tools. It also helps your business to comply with Indian regulatory standards, such as India’s Personal Data Protection Bill, which ensures the secure handling of personal data.

Google Cloud Platform 

Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google. These services include computing, data storage, analytics, and data management tools. GCP provides IaaS, PaaS, and serverless computing environments. Google operates these services from 41 geographical regions worldwide.

GCP is expanding its presence in India, focusing on enterprises seeking advanced data solutions. Some companies that use the Google Cloud Platform in India include Flipkart, Wipro, Media Aigility, Ugam, and Cleartrip. GCP also offers certification programs for individuals and teams who want to showcase their proficiency and expertise in Google Cloud. 

Key Features

  • Data Analytics: BigQuery is an enterprise data warehouse offered by Google that helps to manage and analyze your business data. It has built-in features, including machine learning, search, geospatial analysis, and business intelligence. Using BigQuery, you can process and analyze large datasets in real-time. 
  • Machine Learning: GCP provides TensorFlow and AutoML, which are machine learning services that create and develop ML models. 
  • Global Network: GCP’s global network infrastructure provides your business with a fast, secure, and reliable connection that is useful for high-speed data transfer. 
  • Productivity and Collaboration: The Google Cloud Platform is integrated with Google Workspace, simplifying access management and improving collaboration among distributed teams in different regions.

Azure 

Microsoft Azure is a cloud computing platform that offers a range of cloud-based services and solutions. It allows your organization to build, deploy, and manage applications, including storage, analytics, computing, and networking.

Azure is known for its hybrid and integration capabilities with other Microsoft tools. It offers hybrid solutions like Azure Arc, which allows you to manage and secure resources on-premise, multi-cloud, and edge environments. Additionally, Azure provides integration services such as Logic Apps and API management, enabling smooth connectivity between various applications and systems.  

Key Features

  • Data Storage: Azure Blob Storage is a cloud-based object storage solution optimized for storing extensive unstructured data such as text or binary. Azure also provides other storage products, including data lake storage, NetApp Files, disk storage, container storage, stack edge, and more.
  • Analytics: One of the most prominent analytics tools in the industry is Azure Synapse Analytics. Synapse allows you to integrate key technologies all within one single platform. For instance, SQL to manage and analyze data, Spark for big data processing, Data Explorer for analyzing time-based logs, and Pipelines to implement integration. Azure Synapse also works well with other Azure services, such as Power BI and AzureML, making it a comprehensive analytics tool.  
  • Networking: Azure offers various networking services that can be used in different scenarios based on your needs. One service is Azure Virtual Network, which enables secure communication between on-premises and cloud resources. Another is ExpressRoute, which provides a private connection between your on-premise infrastructure and Azure data centers. 

Oracle Cloud 

Oracle Cloud is a platform for building, deploying, automating, and managing workloads and enterprise applications in the cloud. It offers IaaS, PaaS, SaaS, and data as a service, which you can access as needed over the Internet.  

Oracle Cloud saw a 125% growth in the Indian market during the first half of 2022-23, highlighting significant growth in India. Sectors like telecom, banking, healthcare, manufacturing, and automobile are key industries in India that use Oracle Cloud for growth and innovation.   Now, the company is targeting e-commerce, retail, and startup space, including EdTect, FinTech, and HealtTech. Some top companies that use Oracle cloud services are Infosys, Wipro, KPMG, and Biralsoft. 

Key Features

  • Enterprise-Grade Database Solutions: Oracle Cloud offers an autonomous database, which is a self-managed solution that simplifies database management and enhances performance. It uses ML to automate tasks like backups, security, and database tuning. 
  • High Performance: Oracle’s cloud infrastructure is optimized for high-performance computing workloads, making it ideal for data-intensive applications like analytics and ML. 
  • Security: The Oracle Cloud provides extensive security and compliance features, including IAM, data encryption, advanced threat detection, and governance tools. It also supports local data residency, ensuring sensitive data is secured within specified regions.

IBM Cloud 

IBM Cloud is an enterprise cloud platform that delivers highly resilient, performant, secure, and compliant cloud computing. It combines PaaS with IaaS, providing an integrated experience. The platform scales and supports both small development teams and large enterprise businesses. Available in data centers worldwide, IBM allows you to build and deploy solutions quickly, ensuring reliable performance in a secure, trusted environment. 

Key Features 

  1. Hybrid Cloud Solution: IBM Cloud combines public and private infrastructure, providing flexibility to move workloads based on your organization’s needs. To support a hybrid cloud environment, IBM Cloud uses Red Hat OpenShift, a hybrid cloud container platform that helps you build applications and deploy them anywhere. 
  2. AI and BlockChain: Watson, powered by IBM, provides advanced AI solutions that help your business automate processes and gain insights through NLP and machine learning. IBM also offers blockchain services, including IBM Food Trust, IBM Sterling Transparent Supply, and Hyperledger Fabric Support Edition. These services ensure secure and transparent transactions, enhancing trust and efficiency in your business operations.  
  3. Virtual Private Cloud: IBM’s VPC is a public cloud service that enables you to create a private cloud-like computing environment with a shared public cloud infrastructure. Using VPC, your organization can define and control a virtual network logically isolated from other public cloud tenants. This isolation provides a private space within a public cloud.

Why Choose Cloud Service Providers 

 Here are some of the benefits of opting for a cloud service provider.:

  • Cost Efficiency: Cloud service providers reduce the costs associated with hardware, storage, and maintenance. These providers offer various pricing models tailored to your organization’s work needs. One such model is the pay-as-you-go model, which helps to avoid hefty upfront expenses. 
  • Scalability: Cloud solutions enable your business to scale resources as needed. This supports dynamic work needs without the limitations of physical infrastructure.
  • Accessibility and Collaboration: Cloud platforms allow you to access data securely and in real-time, improving accessibility and connectivity. They also foster remote work and collaboration between teams across various regions within your organization.
  • Maintenance: You don’t have to handle the maintenance, software updates, backups, and security patches, as the cloud service providers manage that for your organization. This helps your data teams focus on core activities.

Conclusion 

Cloud computing is playing an important role in reshaping India’s digital infrastructure. Through cloud computing, you can transform how your business operates to enhance productivity and scalability. Many leading cloud service providers exist, including AWS, GCP, Azure, IBM, and Oracle. By employing the solutions these providers offer within your organization’s infrastructure, you can streamline business tasks, strengthen your market, and meet digital service demands.

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How to Build a Large Language Model in Python

By
Analytics Drift
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Build a Large Language Model

Language models have been revolutionizing human-computer interactions since the early 1980s. With improvements occurring every year, these models are now capable of complex reasoning tasks, summarizing challenging research papers, and translating languages.

Among these models, large language models are the prominent ones that can conduct the most sophisticated operations. This is the key reason for their popularity among various tech enthusiasts and industry professionals.

According to the above Google Trends graph, interest in the term “Large Language Models” has significantly increased in the past five years.

However, creating a custom large language model still remains a difficult task for most users. If the question “How to build a large language model on your own?” lingers in your mind, you have come to the right place!

This article comprehensively discusses the concept of large language models and highlights various methods for building one from scratch.

What Is a Large Language Model?

A Large Language Model, or LLM, is a complex computer program developed to understand and generate human-like text by analyzing patterns in vast datasets. You must train an LLM using deep learning algorithms and large datasets to analyze the behavior of data. This includes learning sentence structures, semantics, and contextual relationships. Once trained, the model predicts the probability of words in a sequence and generates results based on the prompts you provide.

Using the patterns identified in the training data, an LLM computes the probability of each potential response. 

For example, the probability of the occurrence of “Humpty Dumpty sat on a wall” is greater than “Humpty Dumpty wall on a sat.” This is how the model correctly predicts the best-fitting translation of a sentence.

What Are the Characteristics of Large Language Models?

  • Contextual Understanding: LLMs can understand the context of sentences. Rather than relying on words or phrases, these models consider entire sentences or paragraphs to generate the most relevant outcomes.
  • Robust Adaptability: Fine-tuning LLMs makes them adaptable for specific tasks, including content summarization, text generation, and language translation for domains such as legal, medical, and educational.
  • Sentiment Analysis: With LLMs, you can analyze the underlying sentiments involved in the text, identifying whether a statement conveys positive, negative, or neutral emotions. For example, you can analyze the product reviews left by your customers to determine specific business aspects that you can improve on.

What Are the Types of Large Language Models?

Currently, two types of LLMs are popular: the statistical language model and the neural language model.

Statistical language models rely on traditional data modeling techniques, such as N-grams and Markov chains, to learn the probability distribution of words. However, this model is constrained to short sequences, which makes it difficult to produce long contextual content due to their limited scope of memory.

Neural language models, on the other hand, use multiple parameters to predict the next word that best fits a given sequence. Libraries like Keras and frameworks such as TensorFlow provide tools to build and train neural models, creating meaningful associations between words.

What Are N-Gram Models?

N-gram is a statistical language model type that predicts the likelihood of a word based on a sequence of N words.

For example, expressing “Humpty Dumpty sat on a wall” as a Unigram or N=1 results in: 

“Humpty”, “Dumpty”, “sat”, “on”, “a”, “wall” 

On the other hand, utilizing Bigram of N=2, you get: “Humpty Dumpty”, “Dumpty sat”, “sat on”, “on a”, and “a wall”. 

Similarly, an N-gram model would have a sequence of N words.

How Does an N-Gram Model Work?

The N-gram model relies on conditional probability to predict the next word in a sequence. Through this model, you can determine the possibility of the appearance of the word “w” based on its preceding context, “h,” using the formula p(w|h). This formula represents the probability of w appearing given the historical sequence h.

Implementing the N-gram model requires you to:

  • Apply the chain rule of probability.
  • Employ a simplifying assumption to use historical data.

The chain rule allows you to compute the joint probability of a sequence by leveraging conditional probabilities of the previous words.

p(w1, w2, …, wn) = p(w1).p(w2|w1).p(w3|w1,w2)…p(wn|w1,…, wn-1)

Due to the impracticality of calculating probabilities for all possible historical sequences, the model relies on the Markov assumption, simplifying the process.

p(wk|w1,…, wk-1) = p(wk|wk-1)

This implies that the probability of wk depends only on the preceding word wk-1 rather than the entire sequence.

Building an N-Gram Model

Let’s apply the theory by building a basic N-gram language model that uses the Reuters corpus from the Natural Language Toolkit (NLTK).

To get started, open the terminal and install the Python nltk library using the following command:

pip install nltk

Follow these steps to build a large language model from scratch with the N-gram principle:

  • In your code editor, install all the necessary libraries, such as Jupyter Notebook, and download the required datasets.
from nltk.corpus import reuters

from nltk import trigrams

from collections import defaultdict

import nltk

nltk.download('reuters')

nltk.download('punkt')
  • Create a placeholder for the model utilizing the defaultdict subclass. This will store the counts for each trigram.
model = defaultdict(lambda: defaultdict(lambda: 0))
  • Now, you can iterate over all the sentences in the Reuters corpus, convert the sentences into trigrams, and count the number of occurrences of each trigram.
for sentence in reuters.sents():

    for w1, w2, w3 in trigrams(sentence, pad_right=True, pad_left=True):

        model[(w1, w2)][w3] += 1
  • The trigram count is beneficial in generating the probability distribution of the most relevant next word.
for w1_w2 in model:

    total_count = float(sum(model[w1_w2].values()))

    for w3 in model[w1_w2]:

        model[w1_w2][w3] /= total_count
  • To test the results of this model, you can print the likelihood of occurrence of a word next to given two words:
print(dict(model['the', 'cost']))

Output:

{‘of’: 0.816, ‘will’: 0.011, ‘for’: 0.011, ‘-‘: 0.011, ‘savings’: 0.057, ‘effect’: 0.011, ‘.’: 0.011, ‘would’: 0.023, ‘escalation’: 0.011, ‘.”‘: 0.011, ‘down’: 0.011, ‘estimate’: 0.011}

From the above output, the word ‘of’ has the highest probability of appearing after the phrase ‘the cost,’ which makes sense.

In this way, you can create your N-gram model. Although this model is efficient in producing sentences, it has certain limitations.

Limitations of the N-Gram Model

  • Higher values of N enhance the model’s prediction accuracy. However, it also requires more memory and processing power, leading to computational overhead.
  • If the word is unavailable in the training corpus, the probability of the word appearing will be zero, which restricts the generation of new words.

What Are Neural Language Models?

Neural language models are a type of LLM that utilizes neural network architecture to generate responses based on previous data. These models capture semantic relationships between words to produce contextually relevant outputs.

How Does a Neural Language Model Work?

When working with huge data volumes, you can use Recurrent Neural Networks (RNNs). It is a type of machine learning algorithm that enables you to identify the patterns in the input data based on training data.

Composed of multiple layers with interconnected nodes, RNNs have memory elements to keep track of all the training information. However, for long sequences of text, the computational requirements of RNNs become expensive and result in performance degradation.

To overcome this challenge, you can use the Long Short-Term Memory (LSTM) algorithm. This variant of RNN introduces the concept of a “cell” mechanism that retains or discards information in the hidden layers. Each LSTM cell has three gates:

  • Input Gate: Regulates new information flow into the cell.
  • Forget Gate: Determines which information to discard from the memory.
  • Output Gate: Decides which information to transmit as the system’s output.

Building a Neural Language Model

Let’s develop a neural language model using the Python Keras library. Before you begin, you must install the Keras library on your local machine.

pip install keras

Then, follow these steps to build a large language model with Keras:

  • Import the essential libraries in your preferred code editor, such as Jupyter Notebook, to build the model.
import numpy as np

import pandas as pd

from keras.models import Sequential

from keras.layers import Dense, GRU, Embedding
  • Directly read the dataset as a string in a new Jupyter notebook.
data_text = 'Enter your data'
  • For data cleaning, you must preprocess the text to prepare it for model training. These steps can involve converting the text to lowercase, removing punctuation, and eliminating insignificant words.
  • To efficiently model the dataset, consider splitting the data into smaller manageable sequences. For example, you can create a function to create a sequence of 25 characters using clean data obtained from the previous step.
def create_seq(text):

    length = 25

    sequences = list()

    for i in range(length, len(text)):

        seq = text[i-length:i+1]

        sequences.append(seq)

    print('Total Sequences: %d' % len(sequences))

    return sequences

sequences = create_seq(clean_data)
  • Create a character mapping index and an encoding function that converts the textual data into numeric tokens on which the model can train. Execute the following code:
chars = sorted(list(set(clean_data)))

mapping = dict((c, i) for i, c in enumerate(chars))

def encode_seq(seq):

    sequences = list()

    for line in seq:

        encoded_seq = [mapping[char] for char in line]

        sequences.append(encoded_seq)

    return sequences

sequences = encode_seq(sequences)

Running the sequences variable will produce a two-dimensional array of numbers highlighting the encoded values of sequences.

  • After preparing the data, you can now split it into training, testing, and validation sets. To accomplish this, you can either split the data directly utilizing Python indexing or perform the same with methods like train_test_split() from sklearn.model_selection module.
from sklearn.model_selection import train_test_split
X_tr, X_val, y_tr, y_val = train_test_split(encoded_sequences, labels, test_size=0.2, random_state=42)
  • To build a large language model, you can define the model using the Sequential() API and outline its different layers. The embedding layer converts input into dense vectors, the GRU layer defines the RNN architecture, and the dense layer serves as an output interface. You can print the model summary describing its characteristics.
model = Sequential()

model.add(Embedding(vocab, 50, input_length=25, trainable=True))

model.add(GRU(150, recurrent_dropout=0.1, dropout=0.1))

model.add(Dense(vocab, activation='softmax'))

print(model.summary())
  • Compile the model by mentioning the loss function, metrics, and optimizer arguments. This aids in optimizing the model performance.
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
  • Fit the training data to the model by defining the total number of iterations, epochs, and the validation set arguments.
model.fit(X_tr, y_tr, epochs=100, verbose=2, validation_data=(X_val, y_val))
  • Finally, after training, you can use the test data to determine how well this model performs with unseen data. Evaluating the test results is crucial to developing models that generalize effectively across diverse datasets.

Unlike statistical models, neural language models are more efficient at generating new data due to their context-based understanding of the language. However, neural models require technical expertise and significant computational resources. To simplify development, you can leverage the pre-trained models to avoid building from scratch.

Build a Large Language Model Using Hugging Face

The introduction of Generative Adversarial Networks (GANs) and transformer architectures has revolutionized the field of artificial intelligence. GANs utilize two neural networks—a generator and a discriminator—to produce new content. On the other hand, transformers use a self-attention mechanism to process data.

When working with modern LLM architectures like transformers, Hugging Face is a prominent platform. It provides libraries with thousands of pre-trained models for building powerful applications. This reduces the complexity of creating an LLM from scratch.

Along with the model, the Hugging Face platform also offers access to multiple datasets. By integrating your organizational data with these datasets, you can enhance the context-specific relevance of your application.

Key Takeaways

You can build a large language model in Python using different techniques, including statistical, neural language, and pre-trained models. These methods allow you to develop robust LLM applications.

Choose the method for building an LLM based on your needs and the desired level of contextual understanding. However, before getting started with building an LLM, you must ensure that the data is clean to minimize errors and reduce the chances of incorrect or biased outputs.

FAQs

What are some examples of LLMs?

Some popular large language model examples include GPT-4 by OpenAI, BERT by Google AI, Llama by Meta AI, and Claude by Anthropic.

What is the difference between LLM and GPT?

LLM is a broad category of machine learning models trained on massive amounts of text data to understand and generate human-like text. Conversely, a Generative Pre-trained Transformer (GPT) is a specific type of large language model developed by OpenAI.

How do you build a large language model in AI with a prompt context length of 100 trillion words?

Building an LLM with an extended context length will require immense resources. These include data collection, ensuring sufficient computational resources and memory, selecting the appropriate architecture, picking training algorithms, and applying validation strategies.

What is the primary purpose of Large Language Models?

The primary purpose of LLMs is for applications like content creation, code generation, question answering, text classification, and summarization.

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AI as a Service (AIaaS): Comprehensive Guide

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Analytics Drift
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AI as a Service

AI is quickly becoming integral across different industries for various operations, including software development, data analytics, and cybersecurity. According to a Statista report, the global market for AI is expected to exceed USD 826 billion in 2030. 

However, some sectors, such as finance, agriculture, or healthcare, still find deploying AI in their organizational workflow challenging. This is because it requires high technical expertise and monetary resources.

If your organization belongs to any of these sectors, opting for cloud-based AI platforms can be a viable solution. These platforms offer diverse services to simplify the adoption of AI without advanced technical proficiency and at reduced costs.

This article will explain in detail what AI as a Service (AIaaS) is, its different types, and vendors offering AIaaS solutions. This information will help you easily include AI in your operations to foster automation and improve efficiency.

What is AI as a Service?

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AI as a Service is a cloud-hosted service that helps you utilize AI technology to perform various operations in your enterprise. This can include tasks such as sorting resumes for hiring, resolving customer queries, or analyzing ad campaign performance. 

Instead of investing large sums of money into setting up an infrastructure for  AI deployment, you can outsource these services from AIaaS platform vendors. In this way, you can easily leverage AI whether you are working for a small, medium, or large enterprise.

The AIaaS platforms provide services based on deep learning, computer vision, or robotics technology. You can use these technologies to perform business-specific tasks involving NLP, image, or speech recognition.

For example, OpenAI is an AIaaS vendor that offers numerous services, including the highly popular ChatGPT. You can use the ChatGPT to write email campaigns, ad copies, or blogs for your business website.

Types of AI as a Service

There are different types of AI as a Service solution. Some of these are as follows:

Digital Assistants and Bots

Digital assistants are systems that use AI and NLP to generate responses, helping you automate routine tasks like scheduling appointments. Siri, Alexa, and Google Assistant are some examples of popular AI-powered digital assistants.

On the other hand, bots are software programs that mimic human behavior and assist you with activities such as customer support or order management. Chatbots, web crawlers, scrapers, and shopping bots are some of the most common types of bots.

Application Programming Interface (API)

APIs facilitate communication between two or more applications. AI as a Service platform offers different APIs to enable you to include AI functionality without building complex algorithms. These APIs help you connect with AI tools that perform NLP tasks, object recognition,  predictive analytics, and personalized products or content recommendations. Google Cloud Natural Language API and OpenAI GPT API are some examples of AI-powered APIs. 

Machine Learning Frameworks

Several AIaaS platforms offer fully managed machine learning or deep learning frameworks. You can leverage the framework service provider’s cloud servers to collect data, train models, test, and deploy them. 

AIaaS providers also facilitate automated monitoring and version control, ensuring better implementation of MLOps practices. This is in contrast to conventional tools, which require separate solutions for various intermediate processes of ML model development. Amazon Sagemaker AI and Microsoft Azure Machine Learning are some examples of ML frameworks offered by the AIaaS platform.

Vendors Offering AIaaS

Before deploying AI in your business operations, you should know about different vendors offering AI services. Some of the popular AIaaS vendors are as follows:

OpenAI

OpenAI is an AI research organization that offers several AI-powered services. Some of these are:

  • GPT-4o: It is a large language model (LLM) developed by OpenAI that can process text, voice, and image-based data to generate suitable responses. It is available through API, which you can use to develop custom AI applications.
  • OpenAI Codex: OpenAI Codex is an AI programming model that generates code when you provide prompts in natural language. You can utilize this model to write accurate codes.
  • OpenAI DALL-E 2: OpenAI DALL-E 2 is an AI-based text-to-image generating model. You can use it to create realistic and accurate images with high resolution.

Amazon Web Services (AWS)

AWS is a cloud computing service provider that also offers AI and machine learning services. Some of its AIaaS solutions include:

  • Amazon Sagemaker AI: Amazon Sagemaker is a machine learning service that allows you to create, train, and deploy machine learning models. Using Sagemaker, you can handle massive amounts of data in a distributed environment while developing ML pipelines.
  • Amazon Lex: Amazon Lex is an AI service that helps you develop conversational interfaces for voice—and text-based applications. It can process natural language to recognize speech and text, so you do not need deep learning expertise to use it.
  • Amazon Rekognition: Amazon Rekognition is a cloud-based image and video analysis service. It utilizes advanced computer vision and deep learning technology and you can use it for facial or object recognition.
  • Amazon Polly: Amazon Polly allows you to convert text into realistic speeches. It supports various languages, and you can use it to build speech-enabled applications for different regions without language barriers.

Google

Google, a veteran technology company, offers a diverse set of AI and ML services for different use cases. Some of these are:

  • Google Cloud AI: Google Cloud AI is a managed platform that provides you with frameworks like TensorFlow to develop AI or ML models. It offers a scalable infrastructure, helping you to build models of any size. Google Cloud AI is integrated with Google Cloud Dataflow for pre-processing. This enables you to access data from Google Cloud Storage or Google BigQuery.
  • Google Cloud Vision AI: Vision AI is a computer vision service managed by Google Cloud that you can use to automate image and video analytics. Vision AI facilitates facial and object recognition, which is why it finds applications in security or traffic management.
  • Google Dialogflow: Dialogflow is an AI service that you can use to develop conversational agents with generative AI functionality. Using Dialogflow, you can build text- and voice-based agents to increase customer engagement in your business organization.
  • Google Cloud Natural Language AI: Natural Language AI is a service that assists in deriving meaningful business insights from unstructured data, such as text, using Google AutoML solutions. You can use Natural Language AI for sentiment analysis, translations, and for giving content recommendations.

Benefits Offered By AI as a Service

There are numerous benefits of AIaaS that help you to improve the operational efficiency of your organization. Some of these benefits include:

Easy to Deploy

Installing AIaaS is very simple, especially if you or your team have basic or minimal technical knowledge. You can easily integrate AI as a Service tool into your existing system through APIs. 

Some AIaaS platforms offer pre-built models for language processing or predictive analytics functions. You can directly use these models, saving the time and resources required to build them from scratch.

Scalability

AIaaS platforms are cloud-based, so you can easily scale the resources up or down according to your data volume. Many AIaaS platforms also have auto-scaling features that automatically adjust resources per your demand. This is especially helpful if you work for a startup where data volumes fluctuate frequently.

Improves Customer Experience

Some AIaaS tools help you analyze customer data to understand their preferences and purchasing habits. Using this information, you can provide personalized product or content recommendations, which enhances customer retention and reduces churn rates. 

You can utilize AI in customer service through chatbots to respond to customer queries instantly. These chatbots can function 24/7, facilitating customer support around the clock. Several NLP tools are available to classify customer support tickets according to query. You can route these tickets to AI chatbots for resolution, and if the issue is complex, the chatbot can redirect tickets to human customer support staff.

Cost Effective 

Most of the AIaaS platforms offer pay-as-you-go pricing models where you only pay for the resources that you use. You can also avoid unnecessary expenses by understanding the data demand patterns and optimizing the consumption of resources offered by the AIaaS tool.

Challenges of Using AI as a Service Tool

While AIaaS platforms offer numerous advantages, you may also encounter some difficulties when using these tools. Some challenges associated with AIaaS are:

Data Security Risks

AI software requires large amounts of data for training and providing personalized customer experience. This increases the risk of exposing sensitive customer data to cyberattacks and breaches. To avoid this, you must ensure that your AIaaS tool complies with data regulatory frameworks like GDPR or HIPAA. 

Biases in Data

If your datasets are biased, the results generated by the AIaaS tool will be inaccurate. This compromises the outcomes of downstream data operations, leading to a drop in people’s trust in your company. 

Biases occur if your dataset is outdated, inaccurately labeled, or non-representative. You should ensure that the data you collect is inclusive and updated to avoid discrepancies. Proper cleaning and regular auditing enable you to prevent AI hallucinations, a phenomenon in which AI produces misleading results.

Lack of AI Explainability

AI explainability is the capacity of an AI model to explain how it arrived at a specific result. Without AI explainability, AI tools behave like a black box that cannot be interpreted. When you use AIaaS platforms for real-world applications without an explanatory framework, any errorful result generated by the tool can have serious consequences.

For example, if the loan-approving AI tool at your bank rejects loan applications without explaining the reasons, your customers might not know how to proceed further. They will not understand if their application was rejected based on credit score, past defaults, low income, or bias in the training data. This can impact the credibility of your bank. To prevent such discrepancies, you should use AI services that offer explanations for their functions.

Complexity of Integration with Legacy Infrastructure

Integrating AIaaS tools into your existing legacy infrastructure may be challenging. The major reason is legacy systems are not designed for modern API-based integrations and usually lack the computational power to support AI workloads.

As an alternative, you can replace legacy infrastructural environments with modern systems. However, this requires a lot of money and skilled human resources.

Hidden Costs

While some AI models support customization and enable you to use these models for specific use cases, the process can be quite expensive. You might also need to hire AI experts to execute these customizations and pay high compensation for their services.

Furthermore, if you consider migrating to another AIaaS service provider due to increased prices, transferring data and retraining your model can be even more expensive.

Conclusion

AI as a Service has evolved extensively and become a critical component of workflows within different domains such as retail, manufacturing, and even public administration. You have learned about AI as a service, its different types, and several AI service-providing vendors.

While using these AIaaS platforms, you may encounter challenges, such as biases and data security risks. You can overcome these limitations by ensuring that the tools you choose are inclusive and comply with AI regulations. Such practices promote responsible usage of AI and improve your organization’s operational efficiency and profitability.

FAQs

What is Computer Vision, and how is it used to provide AIaaS?

Computer vision is a subdomain of AI that helps computers extract and analyze visual information, such as images and videos. Several platforms, such as Amazon Rekognition and Google Cloud Vision AI, utilize computer vision to offer AIaaS features.

What should you consider when choosing an AIaaS provider?

Before choosing an AIaaS provider, you can consider the types of services offered, ease of integration, scalability, and costs. Ensure the platform you select supports robust security mechanisms and has an active community of users who can help resolve your queries.

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Anthropic Releases a New Citations Feature for Claude

By
Analytics Drift
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Anthropic Introduces Citations Feature

Anthropic, a well-known AI R&D company, has introduced a new Citations feature for its AI reasoning model, Claude. This feature allows users to upload source documents for reference while parsing queries. The model can link responses directly to specific sections of the provided document, improving output accuracy by 15%.

Available through Anthropic API and Google Cloud’s Vertex AI, the Citations feature simplifies the process of integrating source information. Previously, developers had to rely on complex prompt engineering to include references, often resulting in inconsistent results. Now, users can upload PDFs or plain text that gets chunked into sentences before being passed to Claude or use their own pre-chunked data. Claude can analyze the query, reference relevant chunks, and generate responses with precise citations.

Also Read: Anthropic Plans to Release a ‘Two-way’ Voice Mode for Claude

The Citations feature eliminates the need for external file storage and uses Anthropic’s standard token-based pricing model. Users are only charged for input tokens required to process the documents, not output tokens with quoted text.

Companies like Thomas Reuters and Endex are already using the Citations feature. Thomas Reuters employs Claude for its CoCounsel platform, which assists tax and legal practitioners in synthesizing documents and delivering thorough advice. Endex uses the Claude model to power an Autonomous Agent for various financial firms. The team noticed that the Citations feature helped eliminate source hallucinations and formatting issues during multi-stage financial research.

The Citations feature by Anthropic is easy to use and links responses to exact document passages. It is a significant advancement in increasing the trustworthiness of AI-generated outputs.

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ByteDance Launches an Advanced AI Model, Doubao-1.5-pro

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Analytics Drift
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ByteDance Launches Doubao-1.5-pro

On 22nd January 2025, ByteDance launched Doubao-1.5-pro, an advanced AI model that seeks to outperform OpenAI’s reasoning models. Despite the challenges posed by U.S. export restrictions on advanced chips, ByteDance’s model aims to make its mark amidst the competition in the global AI race.

Doubao-1.5-pro claims to surpass OpenAI’s o1 in AIME, a benchmark that evaluates the ability of AI models to understand and respond to complex instructions. The model has shown significant results in areas such as coding, reasoning, knowledge retention, and Chinese language processing.

Available in two configurations–32k and 256k–Doubao-1.5-pro offers aggressive pricing through ByteDance’s Volcano Engine Cloud platform. The model leverages a sparse Mixture-of-Experts (MoE) architecture, where a few active parameters are considered during model training. This allows Doubao-1.5-pro to deliver the performance of a dense model that is seven times its size.

Also Read: OpenAI, SoftBank, and Oracle to build multiple data centers for AI in the U.S.

The ByteDance team has utilized a heterogeneous system design to further enhance model speed and reduce computational requirements. These modifications have allowed Doubao-1.5-pro to optimize tasks like pre-fill decode and attention-FFN to achieve high throughput and low latency.

Doubao-1.5-pro is particularly adept at processing long-form text, making it ideal for several applications, including legal document analysis and academic research. With this model, ByteDance has followed the suit of other Chinese AI firms that have recently contributed to the AI ecosystem. DeepSeek, Moonshot AI, Minimax, and iFlytek have all been praised for their competitive performance against other popular reasoning models. ByteDance’s entry into the market has increased the number of cost-effective, high-performance solutions for complex problem-solving applications.

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