Foundation course on GenAI with Amazon Bedrock and Python

Course Description

This foundational course offers a comprehensive introduction to the exciting world of Generative AI (GenAI), focusing on practical application using Amazon Bedrock and the Python programming language. You'll move from understanding the core concepts that distinguish GenAI from traditional AI to getting hands-on experience with AWS cloud services. The curriculum is designed to be highly practical, guiding you through the Amazon Bedrock playgrounds, prompt engineering techniques, and the architecture of leading Foundation Models (FMs). By the end of the course, you won't just understand the theory; you'll be equipped to build, deploy, and manage your own GenAI applications, including knowledge bases and intelligent agents.

Who is this course for

This course is ideal for a wide range of individuals who are eager to step into the GenAI space, regardless of their current role. It's particularly well-suited for:

• Software Developers & Engineers: Professionals looking to integrate GenAI capabilities into their applications and workflows.

• Data Scientists & Analysts: Individuals who want to leverage large language models (LLMs) and foundation models for advanced data analysis and content generation.

• IT Professionals & Cloud Engineers: Those who want to understand how to deploy and manage AI services within the AWS ecosystem.

• Tech Enthusiasts & Students: Anyone with a foundational knowledge of programming who is curious about the practical applications of generative AI.

• Product Managers: Professionals who need to understand the capabilities of GenAI to drive product innovation.

Course Objectives

By the end of this course, participants will be able to:

• Understand Core Concepts: Clearly articulate the difference between traditional AI and Generative AI, and explain the role of Foundation Models (FMs).

• Navigate the AWS Ecosystem: Set up an AWS account, configure necessary security permissions using IAM, and use the AWS Command Line Interface (CLI).

• Master Prompt Engineering: Design effective prompts for both text and image generation models to achieve desired outcomes.

• Utilize Amazon Bedrock: Confidently use the Amazon Bedrock text, chat, and image playgrounds to interact with various FMs and understand their unique parameters.

• Architect GenAI Solutions: Explain the architecture of Amazon Bedrock and make informed decisions about choosing models (like Claude, Llama, Stability AI) based on performance, cost, and security.

• Build Practical Applications: Develop functional GenAI applications by creating a Knowledge Base and building an intelligent Agent within Amazon Bedrock.

• Integrate and Deploy: Combine all learned components to build and conceptualize end-to-end Generative AI solutions using Python.

Prerequisites

To get the most out of this course, you should have:

1. Basic Python Programming Skills: You should be comfortable with fundamental Python concepts such as variables, data types, loops, functions, and working with libraries.

2. General IT Knowledge: A basic understanding of how applications work and what cloud computing is.

3. No Prior AI/ML or AWS Experience Required: The course is designed to introduce you to both Generative AI and the core AWS services needed for the projects, so you can start from scratch in these areas. However, any prior familiarity with cloud platforms will be beneficial.

Course outline

Section 1: Introduction to Generative Al and Amazon Bedrock

1. What is Al?

2. What is Generative Al?

3. Difference between Al and Generative AI?

4. Understanding Foundation Models (FMs)

5. Introduction to Amazon Bedrock

6. Benefits and Use Cases of Amazon Bedrock

Section 2: Intro to AWS

1. What is cloud and AWS?

2. AWS A/C creation and setup

3. Creating admin IAM user

4. AWS CLI setup

Section 3: Prompt Engineering Quick Intro

1. Prompt pattens for LLMs

2. Different Approaches of Prompt Engineering

3. Prompt Engineering for Text and Image

4. QUIZ DevOps Culture

Section 4: Hands-on with Amazon Bedrock Playground

1. Amazon Bedrock Playgrounds

2. Overview: text and chat

3. Amazon Bedrock Playgrounds Overview: Image

4. Parameters and Considerations of using Models

Section 5: Deep Dive into Amazon Bedrock

1. Service Architecture and Components

2. Quick intro to providers and top models like Claude, Stability, Lalma and Mistral

3. Accessing and Using Bedrock FMs

4. Cost Considerations with Amazon Bedrock

5. Security and Governance with Bedrock

6. Foundation Model Pricing

Section 6: Building Knowledge Base in Amazon Bedrock

1. What is knowledge base in Amazon Bedrock?

Section 7: Building and using GenAl Agents in Amazon Bedrock

Section 8: Building GenAl Applications

Section 9: Putting it All Together

Testimonials