Unlock scalable generative AI with expert training on deploying and optimizing large language models on AWS for peak performance and compliance. 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.
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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.
This course is part of the Large Language Model Operations (LLMOps) Professional Certificate.
What you'll learn
- Deploying large language models on AWS
- Selecting optimal LLM architectures and models
- Optimizing LLM cost, performance, and scalability
- Monitoring and logging LLM metrics
- Building reliable LLM CI/CD pipelines
- Ensuring regulatory compliance for LLM deployment
- Hands-on LLM operationalization using Amazon Bedrock
Syllabus
Week 1: Getting Started with Developing on AWS for AI
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
Week 2: AI Pair Programming from CodeWhisperer to Prompt Engineering
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
Week 3: Amazon Bedrock
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
Week 4: Project Challenges
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
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.
