Building And Evaluating Llm-Powered Apps On Aws

Harness the power of Amazon Bedrock to build, evaluate, and deploy intelligent LLM-powered applications with confidence.

What you'll learn

Design and configure a secure AWS environment

Interact effectively with various Foundation Models

Build and orchestrate intelligent, tool-using agents

Develop and evaluate LLM applications

Requirements

Basic Python Programming: The course will involve writing Python code, primarily using the AWS Boto3 SDK. You don't need to be an expert, but a foundational understanding of Python syntax, data structures (lists, dictionaries), functions, and basic control flow (loops, conditionals) will be essential to follow along with the hands-on labs. Familiarity with AWS Concepts: While we will cover environment setup, a general understanding of core AWS services and concepts will be beneficial. This includes: AWS Account: You will need your own AWS account (a Free Tier account is sufficient for most of the course). We'll guide you through setting it up securely. IAM (Identity and Access Management): Understanding what IAM roles and policies are, and how they grant permissions, will be helpful. We'll specifically cover how to set up permissions for Bedrock. Basic understanding of cloud computing: Familiarity with terms like "cloud," "compute," "storage," and "serverless" will provide a good foundation. Command Line Interface (CLI) Basics: We'll be using the AWS CLI for some configurations and interactions. Basic comfort with navigating your terminal/command prompt and executing commands will be useful. A Text Editor or IDE: You'll need a way to write and edit Python code. Popular choices include VS Code, PyCharm, or even a simple text editor like Sublime Text. Don't worry if you're a beginner in some of these areas! We've designed the course to be hands-on and practical. We'll guide you through each step of setting up your environment and using the tools. The most important thing is a willingness to learn and experiment!

Description

This course, "Building and Evaluating LM-Powered Apps on AWS," offers a comprehensive and practical journey into the world of Large Language Models (LLMs) and their application development on the Amazon Web Services (AWS) cloud, with a strong focus on Amazon Bedrock.You'll begin by gaining a solid understanding of Amazon Bedrock's capabilities as a fully managed service that provides serverless access to a diverse range of high-performing Foundation Models (FMs) from leading AI providers like Anthropic, AI21 Labs, Cohere, and Amazon's own Titan models. We'll demystify why Bedrock is a game-changer for developers, abstracting away the complexities of model hosting and infrastructure management.The core of the course is intensely hands-on. You'll learn to set up a secure and efficient AWS environment for LLM development, including the configuration of IAM roles and permissions, and mastering the use of the AWS Command Line Interface (CLI) and Boto3 SDK for programmatic interaction with Bedrock. This foundational knowledge will empower you to interact directly with various LLMs, experiment with different model parameters (like temperature and top-p), and utilize the Chat Playground for rapid prototyping and prompt engineering.A significant portion of the course is dedicated to building sophisticated LLM applications. You'll dive deep into building intelligent agents using AWS Bedrock Agents, learning how to design their workflows, integrate custom tools via AWS Lambda functions to extend their capabilities (e.g., fetching real-time data or interacting with external APIs), and handle complex, multi-step tasks. You'll also master the art of Retrieval-Augmented Generation (RAG), a powerful technique to enhance LLM responses by grounding them with your own proprietary data. This involves practical steps like embedding and indexing documents in a knowledge base, performing vector searches, and augmenting LLM prompts to generate contextually rich and accurate answers.Crucially, the course doesn't stop at building. You'll learn the vital skill of evaluating your LLM applications. We'll cover various evaluation techniques, including the use of Amazon Bedrock's "LLM-as-a-judge" feature, and methods for running comparisons and scoring outputs. You'll learn to measure key metrics such as response quality, factual correctness (minimizing hallucinations), and relevance to user queries, ensuring your applications are not only functional but also performant and reliable in real-world scenarios.By the conclusion of this course, you will possess the practical skills and confidence to design, develop, deploy, and rigorously evaluate your own intelligent, production-ready, and cost-aware LLM-powered applications on Amazon Bedrock, whether for chatbots, knowledge assistants, or novel generative AI solutions.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Setting Up Your AWS Environment for LLM Development

Lecture 2 Create I AM User

Lecture 3 Install the AWS CLI

Lecture 4 Configure the AWS CLI

Lecture 5 Install boto3

Section 3: Interacting with LLMs on Amazon Bedrock

Lecture 6 Interacting with LLMs on Amazon Bedrock

Section 4: Builduing Agents on AWS

Lecture 7 PART - 1 Building Agents on AWS

This course is ideal for a variety of professionals and enthusiasts who want to leverage the power of Large Language Models (LLMs) on a robust and scalable cloud platform. Specifically, this course is for: Software Developers & Engineers: If you're a developer looking to integrate cutting-edge AI capabilities into your applications and want to understand how to build, deploy, and manage LLM-powered features on AWS, this course will provide you with practical, hands-on skills. Data Scientists & Machine Learning Engineers: For those who understand AI/ML concepts and are looking to transition their knowledge into building real-world generative AI applications without deep infrastructure management, this course offers a clear path to leveraging Amazon Bedrock. AI/ML Enthusiasts & Innovators: If you're passionate about artificial intelligence, fascinated by LLMs, and eager to experiment with building intelligent applications on a leading cloud platform, this course will empower you to bring your ideas to life. Solution Architects & Technical Leads: Professionals responsible for designing system architectures and guiding technical teams will benefit from understanding how Amazon Bedrock simplifies LLM integration, enabling them to make informed decisions about generative AI adoption. In essence, if you're looking to move beyond theoretical understanding of LLMs and dive into the practical aspects of building and evaluating intelligent applications using a managed service like Amazon Bedrock, this course is designed to equip you with the necessary expertise