The public cloud is characterized by its scalability, flexibility, and accessibility, making it an ideal platform for deploying AI and ML solutions. It creates a level playing field for the SMEs (Small and Medium Enterprises) in the race of becoming an AI-first organization.
For instance, a single NVIDIA A100 GPU can cost as much as $20,000, while a server equipped with eight A100 GPUs may exceed $200,000. Additionally, ongoing expenses for power and cooling can accumulate to several thousand dollars annually. In contrast, cloud providers such as AWS, Azure, and GCP (Google Cloud Platform) offer GPU instances on a pay-as-you-go basis. The pay-as-you-go model of public cloud services means that organizations only pay for the resources they use. For example, an AWS p3.16xlarge instance featuring eight NVIDIA V100 GPUs costs approximately $24.48 per hour, which translates to around $214,000 per year if operated continuously.
This is a decisive factor in choosing cloud vs. On-prem for AI applications as it eliminates the need for significant upfront investments in hardware and software. Public cloud providers have data centers around the world allowing organizations to deploy AI/ML solutions closer to their users. This ensures low-latency access to services and data, improving performance and user experience. Let us explore the AI stack of the three top public cloud platforms AWS, Azure, and GCP.
Offerings from the AWS, Azure, and GCP AI Stack
As enterprises lean towards AI development, leading hyperscalers—Amazon Web Services (AWS), Google Cloud, and Microsoft Azure—provide robust solutions to power AI-driven innovations. Their offerings span the three key layers of the AI stack: infrastructure, model access and development, and applications. Let’s explore how each cloud provider contributes to these areas.
AWS AI Stack
Infrastructure Layer
Amazon SageMaker (JumpStart)
A fully managed service offering a complete suite of machine learning (ML) tools to build, train, and deploy models efficiently across various use cases.
Amazon EC2
EC2 compute instances equipped with high-performance GPUs and custom AI chips (Trainium, Inferentia) designed to optimize AI and ML workloads.
Model Access & Development Layer
Amazon Bedrock
Supports a wide range of models from providers like Anthropic, Stability AI, Meta, Cohere, AI21, and Amazon’s proprietary Titan models.
Model Variety
Supports a wide range of models from providers like Anthropic, Stability AI, Meta, Cohere, AI21, and Amazon’s proprietary Titan models.
Application Layer
Amazon Q
A natural language Q&A service that enables users to ask business-related questions and receive precise answers instantly.
CodeWhisperer
An AI-driven code generation and completion tool that helps developers write code faster and with greater accuracy.
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Azure AI Stack
Infrastructure Layer
Azure GPU-Optimized Virtual Machines
High-performance VMs designed to efficiently handle AI and machine learning workloads.
Azure Machine Learning
A comprehensive platform providing tools and services for building, training, and deploying ML models, including automated machine learning, MLOps, and custom model deployment capabilities.
Model Access & Development Layer
Azure OpenAI Service
Offers API access to OpenAI’s advanced models, including GPT-4, enabling use cases such as text generation, translation, and chatbot development.
Azure AI Studio
A robust platform for building and deploying AI solutions at scale, allowing developers to create generative AI applications, collaborate securely, integrate responsible AI practices, and drive AI innovation.
Application Layer
Microsoft 365 Copilot
An AI-powered assistant embedded in Microsoft 365, designed to enhance productivity by assisting with various tasks.
GitHub Copilot
An AI-driven coding assistant that accelerates development by providing intelligent code suggestions and autocompletions.
GCP AI Stack
Infrastructure Layer
Google Cloud GPUs and TPUs
Specialized high-performance hardware optimized for training and deploying AI models efficiently.
Vertex AI
An all-in-one platform that streamlines the development, training, and deployment of machine learning models, integrating essential tools to manage the entire ML lifecycle.
Model Access & Development Layer
Vertex AI PaLM API
Grants access to Google’s advanced foundation models for AI development.
Model Garden
A hub offering a diverse selection of open-source and third-party AI models.
Application Layer
Gemini Code Assist for Google Workspace and Google Cloud
AI-powered tools designed to boost productivity and collaboration within Google Workspace and cloud environments.
AI-Integrated Solutions
AI enhancements embedded across various Google Cloud services to improve functionality and performance.
Shortlisting a Composite AI Stack from Public Cloud
To successfully harness the public cloud for AI and machine learning, organizations should follow these steps:
1. Identify Use Cases and Goals
Start by pinpointing the specific AI/ML use cases that correspond with your business objectives. Whether your focus is on customer segmentation, predictive maintenance, or natural language processing, establishing clear goals will help shape your cloud strategy and resource allocation.
2. Select the Appropriate Cloud Provider
Assess the services offered by leading cloud providers in relation to your specific requirements. Take into account factors such as available machine learning services, pricing structures, data center locations, and how well they integrate with your existing infrastructure.
3. Invest in Skills and Training
Make sure your team possesses the essential skills to effectively utilize cloud-based AI/ML services. This may require training in cloud platforms, machine learning frameworks, and best practices for model development and deployment.
4. Embrace a DevOps Approach
Incorporate AI/ML workflows into your overall DevOps practices to promote seamless collaboration, continuous integration, and continuous deployment (CI/CD). Utilize tools such as Kubernetes for container orchestration and Git for version control to enhance and streamline the development process.
5. Monitor and Optimize
Regularly monitor the performance and costs associated with your AI/ML workloads. Leverage cloud-native monitoring tools to track resource utilization, model performance, and operational metrics. Consistently review and optimize your workflows to enhance efficiency and minimize expenses.
Conclusion
As AI and ML technologies continue to advance, the public cloud will remain a key enabler, offering the resources and tools necessary to harness their full potential and create cloud native AI solutions. Whether you’re a startup developing your first ML model or a large enterprise looking to scale AI capabilities, the public cloud provides the infrastructure to help you achieve your objectives and drive innovation at scale.
