Introduction
Welcome back to our Generative AI series! In the previous article, we provided a framework for selecting the right AI foundation model. In this article, we will explore how public cloud platforms can help with generative AI by examining the offerings from the top three cloud providers: Amazon Web Services (AWS), Google Cloud, and Microsoft Azure.
How Public Cloud Platforms Can Help with GenAI?
With the rise of generative AI, the top hyperscalers — Amazon Web Services (AWS), Google Cloud, and Microsoft Azure — offer comprehensive solutions to support AI initiatives. Let’s break down the offerings of each cloud provider across the three main layers of the generative AI stack: infrastructure, model access/development, and applications.
AWS Generative AI Stack:
1. Infrastructure Layer:
- Amazon SageMaker (JumpStart): A comprehensive service that provides every machine learning (ML) tool needed to build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows.
- Amazon EC2: Amazon EC2 instances with specialized GPUs and custom AI chips (Trainium, Inferentia).
2. Model Access/Development Layer:
- Amazon Bedrock: A service that makes foundation models from leading AI startups and Amazon available via an API. This allows you to access and customize a range of powerful models.
- Model Variety and Choice: Support for models from Anthropic, Stability AI, Meta, Cohere, AI21, and Amazon's own Titan models.
3. Application Layer:
- Amazon Q: A natural language question and answering service that makes it easy to ask business questions in natural language and receive accurate answers quickly.
- CodeWhisperer: An AI-powered code completion and generation service designed to assist developers in writing code more efficiently and accurately.
Azure Generative AI Stack:
1. Infrastructure Layer:
- Azure GPU-enabled virtual machines: High-performance virtual machines optimized for AI workloads.
- Azure Machine Learning: An end-to-end ML platform that offers tools and services for building, training, and deploying ML models. This includes automated machine learning, MLOps, and custom model deployments.
2. Model Access/Development Layer:
- Azure OpenAI Service: Provides access to OpenAI's powerful GPT-4 and other models via an API to support various use cases such as text generation, translations, and chatbots.
- Azure AI Studio: A comprehensive platform for developing and deploying AI solutions at an enterprise scale. It empowers developers to build generative AI applications, explore and deploy AI solutions, collaborate securely, scale AI innovations, and integrate responsible AI practices.
3. Application Layer:
- Microsoft 365 Copilot: An AI assistant integrated into Microsoft 365 to help users with tasks and productivity.
- GitHub Copilot: An AI-powered code completion tool that helps developers write code faster and more accurately.
Google Cloud Generative AI Stack:
1. Infrastructure Layer:
- Google Cloud GPUs and TPUs: High-performance hardware designed for training and deploying AI models.
- Vertex AI: A unified platform for developing, training, and deploying ML models. Vertex AI combines various tools and services to manage and optimize the entire ML lifecycle.
2. Model Access/Development Layer:
- Vertex AI PaLM API: Provides access to Google's powerful foundation models.
- Model Garden: Offers access to various open-source and third-party models.
3. Application Layer:
- Gemini Code Assist (formerly Duet AI) for Google Workspace and Google Cloud: AI-driven enhancements for productivity and collaboration in Google Workspace and cloud services.
- Various AI-powered solutions: Integration of AI capabilities across multiple Google Cloud services to enhance functionality and performance.
Key Differences and Similarities:
- Model Variety: All three cloud providers offer a wide range of models. AWS supports third-party models through Bedrock, Azure focuses on models like OpenAI’s GPT series, and Google Cloud provides access to its PaLM models and other open-source options.
- Infrastructure: Each provider offers specialized hardware for AI workloads. AWS features custom AI chips (Trainium, Inferentia), Azure provides GPU-enabled VMs, and Google Cloud offers high-performance GPUs and TPUs.
- Development Tools: Every platform has robust tools for model development and deployment. AWS utilizes SageMaker, Azure offers Azure AI Studio, and Google Cloud uses Vertex AI, all supporting end-to-end ML workflows.
- Applications: Integration of AI assistants is a common feature. AWS has Amazon Q and CodeWhisperer, Azure includes Microsoft 365 Copilot and GitHub Copilot, while Google Cloud offers Gemini Code Assist (formerly Duet AI) and various other AI-driven solutions.
- Security and Governance: Emphasis on enterprise-grade security and governance is a priority across all platforms, ensuring compliance, data protection, and reliable performance for AI applications.
Conclusion
Selecting the right AI model is crucial for optimizing business outcomes. AWS, Azure, and Google Cloud offer specialized generative AI stacks, each providing unique tools and services to suit different needs. These platforms enable businesses to find, test, and evaluate the best foundation models, ensuring flexibility and scalability for AI initiatives. Partnering with PCG can help businesses navigate cloud-based AI deployment, maximizing the use of these tools. The final article in the Generative AI series will cover how to operationalize AI models to ensure optimal results. Stay tuned for more insights.