This course has been designed to cover the different AWS services that allow you to integrate with large language models and other foundation models. The services covered include:

Amazon Q - A generative AI-powered chatbot that can be customised to suit your needs as a business
Amazon Bedrock - A managed service that allows you to build generative AI applications by accessing a number of different foundation models via APIs
Amazon CodeWhisperer - Enabling you to easily and efficiently code your applications with the help of generated code snippets through machine learning

Learning Objectives:

Amazon Q:

Understand what Amazon Q is
Discover the benefits of leveraging Amazon Q in your enterprise
Explore Amazon Q’s different areas of expertise
Recognise Amazon Q’s pricing plans and features
The various features and use cases
How to configure a new Amazon Q web application that connects to business data systems and repositories
How to interact with an Amazon Q web application
How to control access to Amazon Q within an organization
How to use guardrails and chat controls to enhance the quality of end-user interactions with Amazon Q
How to configure plugins that interact with third-party services directly from Amazon Q
How to work with Amazon Q in the AWS Console and an IDE
How to use Amazon Q with Amazon CodeWhisperer
How to use Amazon Q with data engineering services
How to use Amazon Q with CodeCatalyst

Amazon CodeWhisperer:

Learn what CodeWhisperer does and the benefit it provides
How to generate code snippets with CodeWhisperer
Enable Amazon CodeWhisperer suggestions in the Cloud9 IDE
Utilize Amazon CodeWhisperer suggestions to write and deploy a Python function

Amazon Bedrock:

What Amazon Bedrock allows you to do
The different foundation models supported by Bedrock at the time this lesson was created
How you can experiment running inference with Amazon Playgrounds
How model evaluations allow you to compare and analyze different model performance and response outputs
The different types of model evaluations available and how to configure them
The fundamentals of setting up your APIs with Amazon Bedrock
Learn how to use Python and the Boto3 SDK to interact with the Bedrock API
Learn how to run single prompt inference using code for both image and text generation
Learn how to switch between Foundation Models using code
Understand the invocation options for the Bedrock Runtime

Feedback

We welcome all feedback and suggestions - please contact us at support@cloudacademy.com if you are unsure about where to start or if you would like help getting started.

About the Author

Alana Layton, opens in a new tab

Sr. AWS Content Creator

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Alana Layton is an experienced technical trainer, technical content developer, and cloud engineer living out of Seattle, Washington. Her career has included teaching about AWS all over the world, creating AWS content that is fun, and working in consulting. She currently holds six AWS certifications. Outside of Cloud Academy, you can find her testing her knowledge in bar trivia, reading, or training for a marathon.