Amazon Bedrock - Learn Ai On Aws With Python!

Discover the power of Generative AI on AWS with Amazon Bedrock, create text and images with Python!

What you'll learn

Discover the fundamentals of Amazon Bedrock's AI platform, gaining a comprehensive understanding of its architecture and capabilities.

Learn how to effectively install, configure, and utilize Amazon Bedrock's tools, including an in-depth exploration of text processing with Amazon Titan.

Master the use of advanced AI techniques such as Retrieval Augmented Generation (RAG), and how to apply embeddings and large language models for the real world.

Develop proficiency in extracting and processing complex information from diverse data sources, such as PDF documents and call transcripts

Understand the principles of AI-powered text and image processing, including the exploration of Stability AI parameters and Amazon's Boto3 for image generation

Apply your learned skills in practical, hands-on projects, including the creation of a visual recipe guide.

Requirements

Python and Jupyter Notebook Experience Required

Credit Card Access for AWS (total spend of course is less than $1.00, but CC is required)

Description

Welcome to this course on Amazon Bedrock. This program has been expertly crafted to immerse you in the world of Amazon's AI platform, Bedrock, emphasizing practical Python applications. Suitable for both AI novices and seasoned practitioners, this course promises to deepen your understanding and provide hands-on experience in AI.The course journey commences with an enlightening Section 1, offering an introduction to the course layout, essential resources, and FAQs. This foundational segment is essential for equipping you with the necessary tools and knowledge about Amazon Bedrock, including detailed installation and setup instructions to kickstart your AI adventure.In Section 2, we delve into the complexities of Amazon Bedrock's text models. You'll explore critical text processing parameters and work with Amazon Titan and Llama 2, Bedrock's advanced text modeling tools. This section combines theoretical knowledge with practical application, featuring a project on call transcript analysis and exercises to enhance your skills in extracting and processing information from PDFs.Section 3 transports you to the fascinating world of AI-powered image generation. It covers the essentials of image creation with Stability AI parameters and Amazon's Boto3, including Titan's capabilities in this area. The section's highlight is the Recipe Code Along Project, where you will creatively and technically generate a visual recipe guide.The course culminates in Section 4, focusing on Retrieval Augmented Generation (RAG). This advanced topic is pivotal in AI, and you'll learn about its practical applications and benefits, particularly how Amazon Bedrock integrates embeddings and large language models in RAG.

Overview

Section 1: Course Overview and FAQs

Lecture 1 COURSE DOWNLOADS AND FAQ

Lecture 2 Course Curriculum Overview

Lecture 3 Installation and Set Up

Lecture 4 Amazon Bedrock Overview

Section 2: Text Models with Amazon Bedrock

Lecture 5 Understanding Model Parameters

Lecture 6 Amazon Titan

Lecture 7 Llama 2

Lecture 8 Custom Models

Lecture 9 Code Along Project - Call Transcript

Lecture 10 Ask Questions about PDF - Exercise

Lecture 11 Ask Questions about PDF - Exercise Solution

Section 3: Image Generation

Lecture 12 Understanding Image Generation Parameters

Lecture 13 Stability AI Image Generation with Boto3

Lecture 14 Titan Image Generation

Lecture 15 Titan Image Inpainting

Lecture 16 Image Generation Exercise Overview

Lecture 17 Image Generation Exercise - Solutions

Section 4: RAG- Retrieval Augmented Generation

Lecture 18 Understanding RAG - Retrieval Augmented Generation

Lecture 19 RAG - Example with Amazon Bedrock Embeddings and LLMs

Lecture 20 RAG Exercise Overview

Lecture 21 RAG Exercise Solution

Python developers interested in using Amazon Bedrock Models for Generative AI