# aws-samples/amazon-bedrock-workshop

This is a workshop designed for Amazon Bedrock a foundational model service.

Repository: https://github.com/aws-samples/amazon-bedrock-workshop
Canonical: https://ross.abutalabs.com/products/amazon-bedrock-workshop
Homepage: https://catalog.us-east-1.prod.workshops.aws/amazon-bedrock/en-US
Language: Jupyter Notebook
License: MIT-0
License Family: permissive
Last push: 2026-08-24T14:01:14+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 81
- inputs: {"age_days": 1147, "days_push": 9, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2193, forks 951 (observed 2026-08-28T04:06:25.003979+00:00)

## What it is
A hands-on workshop of Jupyter Notebook labs for Amazon Bedrock, AWS's managed foundation model service. It covers text generation, RAG with Knowledge Bases, model customization/fine-tuning, agentic AI, and serving open-weight models via Bedrock APIs and SDKs.

## Use cases
- learn how to use amazon bedrock apis
- hands-on labs for building rag with bedrock knowledge bases
- learn to build ai agents on aws
- fine-tune foundation models on bedrock
- summarization and code generation with foundation models
- self-paced generative ai workshop for developers
- deploy open-weight models with an openai-compatible endpoint

## When to choose
- you are a developer or solution builder getting started with Amazon Bedrock
- you want guided, self-paced or instructor-led labs covering text generation, RAG, agents, and customization
- you want working notebook code using Bedrock SDKs and APIs

## When to avoid
- you need production-ready application code rather than educational notebooks
- you use a non-AWS model provider or want provider-agnostic tutorials
- you need a finished product or library to import into your codebase

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, rag, agent-framework, llm-inference, llm-training, chatbot
- domain: artificial-intelligence, large-language-models, cloud-computing, tutorials
- platform: python, cloud, self-hosted
- tags: amazon-bedrock, aws, workshop, jupyter-notebooks, generative-ai, foundation-models, hands-on-labs, fine-tuning, knowledge-bases, retrieval-augmented-generation, ai-agents

## Member repositories
- aws-samples/amazon-bedrock-workshop (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:25.003979+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:47:18.520747+00:00, confidence not recorded.
  - readme: https://github.com/aws-samples/amazon-bedrock-workshop (fetched 2026-08-28T04:06:25.003979+00:00, sha 58b32b49157b)
  - homepage: https://catalog.us-east-1.prod.workshops.aws/amazon-bedrock/en-US (fetched 2026-08-29T10:27:43.811271+00:00, sha d95561ca4061)
- Data as of 2026-08-30T08:39:29.467469+00:00.