
Explore generative AI with Amazon Bedrock to build apps via Bedrock APIs and Python. Discover use cases such as text generation, summarization, structured text extraction, image generation, and rag architecture.
Install the prerequisites to access Amazon Bedrock labs by setting up an AWS account, configuring the AWS CLI, installing Python and an editor, and preparing credentials.
Discover how Amazon Bedrock offers a single API to access multiple foundation models in AWS, with data encrypted at rest and guardrails for responsible AI.
For those who are comfortable getting the code from git, use the git clone command as below:
https://github.com/maistrosoft/aws-bedrock-build-gen-ai-apps-course
The entire code for this course is also available as a downloadable zip file in the resources section
Explore retrieval augmented generation (rag) to augment LLMs with external knowledge through vector stores and context-enriched prompts, reducing hallucinations without altering model weights.
Build a rag-based customer service chatbot using retrieval augmented generation with a vector store and product embeddings, compare fine-tuning vs rag, and deploy with Bedrock.
Learn how AI agents autonomously perform tasks and orchestrate workflows with functions to interact with external environments. See how agents and LLMs enable travel planning, weather checks, and bookings.
Explore multimodal LMs that respond with text, images, and video, and learn to interpret visuals or generate images from text for advertisements from text using Amazon Nova on Bedrock.
Explore bedrock data automation, automating multimodal data processing from text to video with cleansing, transformation, redaction, and governance, including rag-based retrieval for knowledge bases.
Programmatically build and manage guardrails, configure topics and content policy filters, set input/output thresholds, and block messages at input or output stages.
Apply generative AI with Amazon Bedrock through two hands-on exercises: a kids library chatbot and a car dealer sales agent using rag sources and purchasing options.
Explore how to build generative AI apps with Amazon Bedrock using rag architecture in Python, and apply multimodal, intelligent document processing, and AI agent use cases.
Discover a short bonus lecture from Connected World, highlighting training, software development, and consulting in artificial intelligence and machine learning.
Welcome to Introduction to Generative AI with Amazon Bedrock.
We are living in a very exciting time.
In the last few years, large language models, or LLMs for short, have revolutionized AI.
It is now possible for AI to have conversations with humans, do simple reasoning, translate text, generate pictures, generate video, write code and so on.
This has created a huge white space for entrepreneurs and developers to build exciting business applications.
In this short course, you will look at how to build Generative AI apps with Amazon Bedrock, using Python.
You will get an introduction to Amazon Bedrock, the Bedrock API, interacting with LLMs using Bedrock, prompting, building Gen AI apps using the RAG architecture and much more.
I’m Varad, your instructor for this course. I’m an ex-AWS solutions architect and a certified AI / ML specialist. I have spent many years with leaders in SAAS, Cloud and IT Consulting. I have designed and developed AI / ML solutions, led multi million-dollar technology projects, and guided CXOs on Digital transformation initiatives.
This is a beginner level course in Generative AI using Amazon Bedrock. It will benefit users who want to get a quick understanding of how to interact with Amazon Bedrock using Python.
Note that this is a basic course on Amazon Bedrock. Ideally, this should be your first course in Bedrock.
If you are looking for more advanced coverage of Bedrock, this is not the course for it.
If you are a programmer, this will get you started on how to build applications with Amazon Bedrock.
If you are a manager or a non technical user, you will still benefit from this course by understanding what you can do with Amazon Bedrock.
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