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?
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, 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.
