Cloud Platforms for GenAI (Azure, AWS, Google Cloud)

Deploy scalable Generative AI solutions using cloud-native services.

Pillar

Technology – Platforms, Tools, Infrastructure & Productivity

Overview

This course provides an in-depth understanding of how to leverage major cloud platforms—Microsoft Azure, Amazon Web Services (AWS), and Google Cloud—to build, deploy, and scale Generative AI applications. Participants will explore cloud-native AI tools, services, and best practices for efficient, secure, and cost-effective GenAI implementations.

Learning Objectives

Participants will be able to:

• Understand the AI and GenAI services offered by Azure, AWS, and Google Cloud

• Architect scalable GenAI solutions using cloud infrastructure

• Deploy and manage GenAI workloads leveraging serverless and containerized environments

• Implement security, compliance, and cost optimization strategies for AI deployments

• Monitor and optimize GenAI applications for performance and reliability

Target Audience

• Cloud architects and engineers

• AI/ML developers and data scientists

• IT managers and technical leads

• DevOps and MLOps professionals

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures and platform demos

• Hands-on labs deploying GenAI models on cloud services

• Case studies and architecture workshops

• Group discussions on cloud AI governance and compliance

Materials Provided

• Cloud platform AI service guides and setup instructions

• Sample deployment templates and code snippets

• Access to sandbox environments for practical exercises

• Course slides and reference documentation

Outcomes

• Design and deploy scalable GenAI applications on Azure, AWS, and Google Cloud

• Leverage cloud AI tools and frameworks efficiently

• Manage security, compliance, and cost in cloud GenAI projects

• Maintain and monitor AI workloads for optimal business value

Outline / Content

Day 1: Overview of Cloud AI Platforms

• Introduction to Azure AI, AWS AI & ML, and Google Cloud AI services

• Key differences and selecting the right platform for your needs

• Cloud concepts: Compute, storage, and networking for AI

Day 2: Deploying GenAI Models on Cloud

• Using Azure AI Studio, AWS Bedrock, and Google Vertex AI

• Containerization and serverless AI deployments

• API integration and scaling GenAI workloads

Day 3: Security, Compliance, and Cost Management

• Identity and access management for AI services

• Data privacy, encryption, and compliance frameworks

• Cost optimization strategies and monitoring

Day 4: Monitoring, Optimization, and Case Studies

• Performance monitoring and logging GenAI workloads

• Troubleshooting and auto-scaling best practices

• Real-world use cases and architecture design workshop