Developing Generative AI Applications on AWS - 2 Days ILT

In this course, you will learn how to build and deploy generative AI applications using AWS services like Amazon SageMaker, AWS Lambda, and other AI-driven tools. Gain hands-on experience in creating real-time AI solutions tailored to business needs.

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Level: Advanced

Duration: 2 Days

Delivery Type: Instructor-Led Training

• Describe generative AI, its alignment with machine learning, and its risks and benefits.
• Identify business value and use cases for generative AI.
• Understand Amazon Bedrock, its benefits, use cases, architecture, and cost structure.
• Implement Amazon Bedrock in the AWS Management Console.
• Define prompt engineering, best practices, and advanced prompt techniques.
• Analyze bias in FM responses and design prompts to mitigate it.
• Identify components of generative AI applications and customize foundation models.
• Explore Amazon Bedrock APIs, inference parameters, and AWS security offerings.
• Integrate LangChain with LLMs, chat models, retrievers, and Agents for Amazon Bedrock.
• Apply architecture patterns and build sample generative AI applications using RAG.

• Software Developer

• AWS Technical Essentials 
• Intermediate-level proficiency in Python

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 Model-specific prompt techniques Demonstration: Fine-tuning a basic text prompt Addressing prompt misuses Mitigating bias Demonstration: Image bias mitigation

Day 2

Module 5: Amazon Bedrock Application Components

Overview of generative AI application components Foundation models and the FM interface Working with datasets and embeddings Demonstration: Word embeddings Additional application components Retrieval Augmented Generation (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 Using models with LangChain Constructing prompts Demonstration: Bedrock with LangChain using a prompt that includes context 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 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 Chatbot 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