Open-source technologies have transformed the software development and deployment process by promoting accessibility, collaboration, transparency, and cost-effectiveness. These features have significantly accelerated technology adoption and are now driving advancements in the field of AI.
In a survey conducted by IBM, more than 80% of respondents confirmed that their organizations use open-source AI tools, reflecting the growing utilization of AI. There are several open-source libraries, frameworks, and low- or no-code platforms that have simplified the integration of AI into existing workflows.
Let’s learn about nine such open-source AI platforms in detail. You can leverage any of these solutions to build chatbots, perform predictive analytics, or automate your routine business operations.
What is Open-Source AI?
Open-source AI refers to AI systems or frameworks that offer freely accessible source code, training data, and models. These enable you to modify their source code and utilize them for downstream applications.
Unlike paid or proprietary AI systems, open-source AI solutions foster community-driven development and collaboration in a cost-effective manner. Such tools promote transparency and innovation in your organizational workflows.
PyTorch, Keras, OpenCV, and H2O.ai are some popular examples of open-source AI systems.
Advantages of Using Open-Source AI
Using open-source AI platforms can benefit your business in several ways. Some of these advantages include:
Enhanced Accessibility
If you have a limited budget and resources, open-source AI tools offer easy access to advanced AI technologies. Even if you own a small business or startup, you can utilize these tools to experiment, innovate, and build products at lower costs.
Flexibility
To develop tailored applications, you can customize open-source AI tools according to your specific requirements. You can easily integrate these platforms into your existing infrastructure and avoid relying on a single vendor for all the data-related tasks. This provides you with the freedom to choose suitable solutions for data processing and AI implementation.
Transparency
Since the source code of open-source AI platforms is openly available, anyone in your team can access, audit, and modify it. Such scrutiny fosters transparency and accountability. Through this practice, you can ensure secure and responsible usage of AI.
Cost-Effectiveness
Open-source AI solutions eliminate the need for licensing fees, making them suitable for startups or small enterprises. The open-source AI tools are designed in such a way that you can easily deploy them in varied environments without additional infrastructure. You can also scale these tools according to your data load. All these capabilities make open-source AI platforms cost-effective solutions.
Challenges of Utilizing Open-Source AI
Despite remarkable benefits, there are some disadvantages associated with open-source AI. Some of these challenges are:
Lack of Professional Support
Open-source AI models may contain hidden bugs that can compromise their performance or accuracy. Even if you get community support for troubleshooting, open-source AI software often lacks dedicated professional support teams. Such tools also do not have standard documentation, leading to operational inconsistencies.
Data Security Risks
The training datasets used in open-source AI models may contain sensitive information, increasing the risk of data breaches and cyberattacks. Open-source AI software are also more susceptible to malicious attacks since anyone can access and corrupt the source code.
Maintenance Responsibility
To utilize open-source AI solutions for high-volume workloads, you need to invest in computational resources for training and deployment. This can be challenging, especially if you have smaller businesses or startups with limited budgets.
Top Open-Source AI Platforms
Several exceptional open-source AI platforms are available for developing machine learning models and AI applications. Here are some such prominent systems:
1. TensorFlow
TensorFlow is an open-source framework for machine learning and deep learning. You can use it to train ML models on tensor-based data using multiple programming languages, including Python and JavaScript. This provides you the flexibility to use TensorFlow for diverse projects without language restrictions. It is used to develop ML models for image recognition, natural language processing (NLP), and video detection.
Computational graphs form an important component of TensorFlow and represent the data flow during ML model training. The nodes in these graphs represent mathematical operations, while the edges illustrate the data (tensors) moving between these operations. Such computational graphs allow you to efficiently monitor and optimize the model development process.
2. PyTorch
PyTorch is a Python-based machine learning library that you can use to train deep learning models. It leverages Torch, an open-source library in Lua scripting language, to facilitate the creation of neural networks. You can quickly train these neural networks in PyTorch, as it supports GPU acceleration for faster model training.
While training a deep learning model, you need to use the backpropagation method to reduce the difference between the model’s predicted and original outcomes. This involves adjusting weights and biases in the model using algorithms like gradient descent. PyTorch offers a built-in differentiation engine called torch.autograd to calculate the gradient. As a result, you can use PyTorch to build applications based on robust deep learning models.
3. OpenAI GPT
OpenAI’s ChatGPT (Generative Pre-trained Transformer) is a generative AI chatbot developed in 2022. It is trained on extensive datasets consisting of texts, images, audio, and video information. To use GPT, you need to first provide suitable prompts as input. The model then utilizes techniques such as tokenization, normalization, and backpropagation to provide outputs. Since its launch, OpenAI has released several versions of ChatGPT with higher speed and accuracy.
Using GPT, you can perform several NLP tasks, including text generation, summarization, and translation. With OpenAI’s APIs, you can also utilize GPT to develop virtual assistants and chatbots. These are useful for numerous use cases like automated customer support and building a social media sentiment analyzer.
4. Keras
Keras is a Python-based neural network library that helps you develop deep learning models. You can connect it with popular deep-learning frameworks through APIs. To allow you to build and deploy ML models quickly, Keras supports TensorFlow, PyTorch, and JAX frameworks for managing backend operations.
If you are a beginner, Keras is a suitable solution for quick prototyping and deployment of ML models. Owing to its simplicity and user-friendly interface, Keras is also used to teach deep learning concepts in academic courses.
5. OpenCV
Open Source Computer Vision, commonly known as OpenCV, is a machine learning and computer vision library. It was developed to offer a common infrastructure for building computer vision-based applications like object detection, medical imaging, and facial recognition. OpenCV supports more than 2,500 algorithms for image and video analysis tasks.
During object recognition, you have to deal with unstructured data, including images and videos. You can store this data in vector databases. Then, you can query and analyze it using OpenCV with other libraries like NumPy and Pandas. This makes OpenCV useful in applications involving image processing, video analysis, and real-time facial recognition. It is used by several well-known organizations, including Google, Microsoft, IBM, and Honda.
6. Rasa
Rasa is an open-source ML framework that helps you develop conversational AI assistants and chatbots. You can utilize the YAML language while using Rasa to manage training data, including Natural Language Understanding (NLU) and conversational data. NLU consists of intents and entities. Intent represents the purpose of the user inputs, while entities are relevant pieces of information that can be extracted from the inputs.
On the other hand, conversational data contains stories and rules. Stories are a type of training data that ML models use to identify patterns in conversations and generalize responses from unknown dialogue paths. Conversely, rules define specific paths for structured dialogues, making them essential for chatbot response management. Such efficient features make Rasa a suitable solution for building open-source AI chatbots for customer support, lead generation, and e-commerce assistance.
7. H2O.ai
H2o.ai is a distributed and scalable machine learning and predictive analytics platform. You can use it to develop big data-based machine learning models and deploy them in enterprise environments. To facilitate this, H2o.ai supports various supervised and unsupervised ML algorithms. Common ones include AutoML, Naive Bayes classifier, and K-means clustering. H2o.ai’s data parser can detect the schema of the dataset that is to be ingested. This allows you to analyze data from various sources in diverse formats.
H2o.ai provides a user-friendly interface, visual model workflows, and easy API integration for developing high-quality ML models. You can use it to build models for fraud detection, customer churn prediction, and cash management.
8. MLflow
MLflow is an end-to-end machine learning workflow management platform that offers features for tracking, packaging, and deploying ML models. Using MLflow Tracking API and UI, you can log parameters, code versions, metrics, and output files while running machine learning codes. The executions of small pieces of code are called runs, and a group of runs for a specific task is known as an experiment.
You can track these runs using functions such as mlflow.start_run(), mlflow.log_param(), and mlflow.log_metric(). All these functionalities and features enable you to compare and monitor the performance of different models. The runs are reproducible, facilitating quick replication and verification of experiments and fostering collaboration.
9. KNIME
Konstanz Information Miner, or KNIME, is an AI-powered data integration, analytics, and reporting solution. It is a no-code platform that allows you to build complex data workflows related to GenAI and machine learning using its drag-and-drop functionalities. As a result, you do not need to have extensive knowledge of programming language to use KNIME.
You can integrate data from various sources, including databases, flat files, and cloud-based data systems, with KNIME to analyze diverse datasets. It also enables you to expand its features by supporting various plugins and extensions. Due to such versatility, you can use KNIME across various sectors, including life sciences, financial services, healthcare, and retail.
Best Practices For Using AI Tools
To effectively leverage AI platforms, you should follow some best practices that promote transparency, security, and sustainability. Here are some recommendations:
Adopt Explainable AI Technologies
Explainable AI (XAI) consists of a set of techniques that you can use to build AI and ML models that provide clear reasoning behind their predictions. This ensures responsible use of AI by fostering transparency and accountability. For AI explainability, you can utilize several techniques, including SHAP and LIME. These processes provide a clear explanation of model predictions, especially in critical domains like healthcare, finance, and law.
Prioritize Robust Data Security
Before using open-source AI software, you must ensure that its source code is audited through a trusted and independent auditor. You must download or install any AI software only from official or verified sources. To avoid security lapses and to access enhanced features, regularly monitor and update all your AI models. Along with this, you should deploy data regulatory frameworks such as GDPR and HIPAA to maintain data privacy.
Sustainable Deployment
While deploying AI tools in your workflows, you should be aware of their impact on the environment, economy, and society. To avoid harming the ecosystem, you should optimize energy consumption by minimizing the use of hardware and opting to use cloud-based services. Scale your AI workflows only according to the volume of your data workloads and try to reuse AI/ML models to reduce resource consumption.
Conclusion
The need of open-source AI software is increasing due to the collaborative, innovative, and transparent use of AI. This blog comprehensively explains what open-source AI is, along with the nine most popular open-source AI platforms. These solutions offer several advantages, such as increased and cost-effective usage of technologies.
However, there are some disadvantages to using open-source solutions, including compromises in data security and infrastructural complexities. To overcome these limitations, you can implement best practices such as the inclusion of explainable AI techniques and robust security measures. Through this approach, you can use open-source AI effectively for your business growth.
FAQs
What are some open-source generative AI models?
There are several open-source generative AI models available, and you can use any of them for your target use case. Some popular genAI models include Llama 3, Stable Diffusion, Mistral AI, ChatGPT, and Falcon.
What trends will be observed in open-source AI models in the future?
In the future, the following trends will be observed in open-source AI usage, according to AI experts:
- Development of AI models in local languages
- Use of high-quality training datasets
- Increase in agentic workflow trends
- Enhanced collaboration
- Adoption of Explainable AI practices
