Building Generative AI with AWS: Amazon Q Developer, Bedrock Inference, and SageMaker Canvas
1h 23mIntermediate2025-01-16
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Pragmatic AI Labs
Course details
In this course, MLOps expert Noah Gift guides you through the world of generative AI on AWS. Build your understanding of tokenization, multiple model architecture, and how AI models are built and deployed. Discover the innovative Retrieval-Augmented Generation (RAG) technique and see its implementation on AWS using Bedrock knowledge agents. Get hands-on with Amazon Q developer tools, including installation and development using IntelliJ and VS Code, as well as features like the documentation assistant and code scanning. Dive deeper into AWS Bedrock with lessons on provisioned IO and evaluating prompts. Familiarize yourself with SageMaker Canvas, a robust environment for working with datasets and MLOps. By the end of this course, you will be proficient in leveraging various AWS tools to optimize your AI and machine learning workflows.
Skills covered
Amazon BedrockArtificial Intelligence for DesignCloud DevelopmentAmazon Web Services (AWS)AI Productivity ToolsAmazonGenerative AIVideoCloud ServicesPhotographyGraphic DesignCloud PlatformsArtificial Intelligence (AI)Animation and IllustrationCloud ComputingBusiness Software and ToolsOne-Off
Concepts
Module 1 - Foundation Models and Core Concepts
- 01 - Generative AI on AWS
- 02 - Understanding tokenization
- 03 - Multiple model architecture
- 04 - Introduction to RAG
- 05 - RAG on AWS
- 06 - RAG with Bedrock knowledge agent
- 07 - RAG Bedrock system walkthrough
- 08 - AWS Bedrock rust demo
- 09 - Bedrock rust architecture
Module 2 - Amazon Q Developer Tools
- 10 - Amazon Q developer introduction
- 11 - Amazon Q in IntelliJ
- 12 - Installing Amazon Q in VS Code
- 13 - Development with Amazon Q
- 14 - Documentation assistant
- 15 - Amazon Q code scanning
Module 3 - AWS Bedrock
- 16 - Bedrock provisioned IO
- 17 - Setting up Bedrock provisioned IO
- 18 - Evaluating prompts in Bedrock
Module 4 - SageMaker Canvas and MLOps
- 19 - SageMaker canvas introduction
- 20 - Canvas UI overview
- 21 - Working with datasets
- 22 - Course conclusion