Developing Generative AI Applications on AWS

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Szkolenie AWS_DGAIA_Z

Developing Generative AI Applications on AWS

Rozwiązania IT AWS Dane, Analityka, AI i ML Chmura obliczeniowa Dane, Analityka, AI i ML Dane, Analityka, AI i ML

Akredytacja

Opis szkolenia

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.

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

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

Zakres

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 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
- Demonstration: Invoke 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
- Demonstration: Bedrock with LangChain Using a Prompt that Includes Context
Module 8: Architecture Patterns
- Introduction to architecture patterns
- Text summarization
- Demonstration: Text Summarization of Small Files with Anthropic Claude
- Demonstration: Abstractive Text Summarization with Amazon Titan Using LangChain
- Question answering
- Demonstration: Using Amazon Bedrock for Question Answering
- Chatbots
- Demonstration: Conversational Interface – Chatbot with AI21 LLM
- Code generation
- Demonstration: Using Amazon Bedrock Models for Code Generation
- LangChain and agents for Amazon Bedrock
- Demonstration: Integrating Amazon Bedrock Models with LangChain Agents

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