Module 1 Labs: Hands-on with Amazon Bedrock

The chapters in this module build the concepts; the labs turn them into working code on Amazon Bedrock. The notebooks below are the original AWS Machine Learning University lab notebooks, included here so you can read the code alongside the theory. Each maps to a lesson you have just studied.

Lab	Notebook	Connects to
2a	Lab 2a: Introduction to Amazon Bedrock	Ch. 1-2: calling Bedrock foundation models with Boto3.
2b	Lab 2b: Chat with Amazon Bedrock	Ch. 2: a simple conversational application on a decoder-only model.
3	Lab 3: Prompt Engineering	Ch. 3: standard prompt-engineering techniques and inference parameters.
4a	Lab 4a: Self-Consistency	Ch. 4: the self-consistency technique.
4b	Lab 4b: Tree-of-Thought	Ch. 4: the tree-of-thought technique.
5	Lab 5: Multimodal Prompting	Ch. 5: multimodal prompting with images and text.

Expected error: “AccessDeniedException” (model access not enabled)

Some lab cells may display an error like:

AccessDeniedException: An error occurred (AccessDeniedException) when calling
the InvokeModel operation: Model access is denied due to IAM user or service
role is not authorized to perform the required AWS Marketplace actions
(aws-marketplace:ViewSubscriptions, aws-marketplace:Subscribe) to enable
access to this model.

This is not a bug in the lab code. It means your AWS account or IAM role has not enabled access to that specific foundation model in Amazon Bedrock. To fix it: go to the Amazon Bedrock console -> Model access, request or enable the model, make sure your role has the aws-marketplace:ViewSubscriptions and aws-marketplace:Subscribe permissions, and re-run the cell after a few minutes. Model availability and the exact permissions required vary by AWS Region and account, so consult the Amazon Bedrock documentation for your setup.

Running the labs

These notebooks call live Amazon Bedrock endpoints, so they are rendered here for reading but are not executed during the book build. To run them, open the notebook in an environment with AWS credentials and Bedrock model access enabled, ideally Amazon SageMaker with the conda_python3 kernel, and install the packages listed in the labs’ requirements.txt. Lab 1 is a console walkthrough; follow it directly in the Amazon Bedrock console.

A note on the utilities

Several notebooks import helpers from a local mlu_utils package (quiz widgets, prompt helpers, and images). That folder ships alongside the notebooks in this book so the imports and figures resolve correctly when you open them.

Work through the labs in order, reading each lesson’s chapter first. When you finish Lab 5, you will have invoked Bedrock models programmatically, engineered and tuned prompts, implemented two advanced reasoning techniques, and prompted a multimodal model, the practical foundation for Modules 2 and 3.