AWS-DEVGENAI | Developing Generative AI Applications on AWS
 
This course is designed to introduce generative AI to software developers interested in leveraging large language models without fine-tuning. The course provides an overview of generative AI, planning a generative AI project, getting started with Amazon Bedrock, the foundations of prompt engineering, and the architecture patterns to build generative AI applications using Amazon Bedrock and LangChain.
 
Course objectives
In this course, you will learn to:
Describe generative AI and how it aligns to machine learning.
Define the importance of generative AI and explain its potential risks and benefits.
Identify business value from generative AI use cases.
Discuss the technical foundations and key terminology for generative AI.
Explain the steps for planning a generative AI project.
Identify some of the risks and mitigations when using generative AI.
Understand how Amazon Bedrock works.
Familiarize yourself with basic concepts of Amazon Bedrock.
Recognize the benefits of Amazon Bedrock.
List typical use cases for Amazon Bedrock.
Describe the typical architecture associated with an Amazon Bedrock solution.
Understand the cost structure of Amazon Bedrock.
Implement a demonstration of Amazon Bedrock in the AWS Management Console.
Define prompt engineering and apply general best practices when interacting with FMs.
Identify the basic types of prompt techniques, including zero-shot and few-shot learning.
Apply advanced prompt techniques when necessary for your use case.
Identify which prompt-techniques are best-suited for specific models.
Identify potential prompt misuses.
Analyze potential bias in FM responses and design prompts that mitigate that bias.
Identify the components of a generative AI application and how to customize a foundation model (FM).
Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs.
Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications.
Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock.
Describe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applications.
Apply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach.
 
Intended audience
This course is intended for:
Software developers interested in leveraging large language models without fine-tuning.
 
Prerequisites
We recommend that attendees of this course have:
AWS Technical Essentials
Intermediate-level proficiency in Python
 
Course Outline
Day 1
Module 1: Introduction to Generative AI - Art of the Possible
Overview of ML
Basics of generative AI
Generative AI use cases
Generative AI in practice
Risks and benefits
Module 2: Planning a Generative AI Project
Generative AI fundamentals
Generative AI in practice
Generative AI context
Steps in planning a generative AI project
Risks and mitigation
Module 3: Getting Started with Amazon Bedrock
Introduction to Amazon Bedrock
Architecture and use cases
How to use Amazon Bedrock
Demonstration: Setting Up Amazon Bedrock Access and Using Playgrounds
Module 4: Foundations of Prompt Engineering
Basics of foundation models
Fundamentals of prompt engineering
Basic prompt techniques
Advanced prompt techniques
Demonstration: Fine-Tuning a Basic Text Prompt
Model-specific prompt techniques
Addressing prompt misuses
Mitigating bias
Demonstration: Image Bias-Mitigation
Day 2
Module 5: Amazon Bedrock Application Components
Applications and use cases
Overview of generative AI application components
Foundation models and the FM interface
Working with datasets and embeddings
Demonstration: Word Embeddings
Additional application components
RAG
Model fine-tuning
Securing generative AI applications
Generative AI application architecture
Module 6: Amazon Bedrock Foundation Models
Introduction to Amazon Bedrock foundation models
Using Amazon Bedrock FMs for inference
Amazon Bedrock methods
Data protection and auditability
Lab: Invoke Amazon Bedrock model for text generation using zero-shot prompt
Module 7: LangChain
Optimizing LLM performance
Integrating AWS and LangChain
Using models with LangChain
Constructing prompts
Structuring documents with indexes
Storing and retrieving data with memory
Using chains to sequence components
Managing external resources with LangChain agents
Module 8: Architecture Patterns
Introduction to architecture patterns
Text summarization
Lab: Using Amazon Titan Text Premier to summarize text of small files
Lab: Summarize long texts with Amazon Titan
Question answering
Lab: Using Amazon Bedrock for question answering
Chatbots
Lab: Build a chatbot
Code generation
Lab: Using Amazon Bedrock Models for Code Generation
LangChain and agents for Amazon Bedrock
Lab: Building conversational applications with the Converse API
 
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Debido a las constantes actualizaciones de los contenidos de los cursos por parte del fabricante, el contenido de este temario puede variar con respecto al publicado en el sitio oficial, sin embargo, Netec siempre entregará la versión actualizada de éste.