Generative AI and LLMs on AWS

Master deploying generative AI models like GPT on AWS through hands-on labs. Learn architecture selection, cost optimization, monitoring, CI/CD pipelines, and compliance best practices. Gain skills in operationalizing LLMs using Amazon Bedrock, auto-scaling, spot instances, and differential privacy techniques. Ideal for ML engineers, data scientists, and technical leaders.

Course Highlights:

• Choose optimal LLM architectures for your applications
• Optimize cost, performance and scalability with auto-scaling and orchestration
• Monitor LLM metrics and continuously improve model quality
• Build secure CI/CD pipelines to train, deploy and update LLMs
• Ensure regulatory compliance via differential privacy and controlled rollouts
• Real-world, hands-on training for production-ready generative AI

Unlock the power of large language models on AWS. Master operationalization using cloud-native services through this comprehensive, practical training program.

Week 1: Getting Started with Developing on AWS for AI

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• Introduction to AWS Cloud Computing for AI, including the AWS Cloud Adoption Framework

• Setting up AI-focused development environments using AWS services like Cloud9, SageMaker, and Lightsail

• Developing serverless solutions for data, ML, and AI using AWS Bedrock and Rust

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Week 2: AI Pair Programming from CodeWhisperer to Prompt Engineering

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• Learning prompt engineering techniques to guide large language models

• Using AWS CodeWhisperer as an AI pair programming assistant

• Leveraging CodeWhisperer CLI to automate tasks and build efficient Bash scripts

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Week 3: Amazon Bedrock

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• Key capabilities and components of Amazon Bedrock

• Accessing and invoking Bedrock foundation models using AWS CLI, Boto3 Python SDK, and Rust SDK

• Prompt engineering and model evaluation to optimize Bedrock model performance

• Customizing models with fine-tuning and knowledge bases

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Week 4: Project Challenges

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• Applying course concepts to build an end-to-end AI workflow

• Developing Rust functions for Bedrock agents and integrating into an orchestration flow

• Debugging, benchmarking, and prompt engineering to optimize a deployed AI application on AWS

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By the end of this course, you will have gained hands-on experience with cutting-edge AI/ML tools on AWS like Bedrock, CodeWhisperer, and Rust. You'll be able to build and deploy efficient, serverless AI applications in production.