Developing Generative AI Applications on AWS

Developing Generative AI Applications on AWSDGAIAAWAmazon Web ServicesAW-DGAIA2.0In this course, you will learn to do the following: <ul> <li>Develop generative AI applications using Amazon Bedrock.</li><li>Design architecture patterns of generative AI applications.</li><li>Configure Amazon Bedrock APIs to invoke foundation models (FMs) programmatically.</li><li>Develop agentic AI applications by integrating Amazon Bedrock tools and open source frameworks.</li><li>Build custom solutions with Retrieval Augmented Generation (RAG) and Amazon Bedrock Knowledge Bases.</li><li>Integrate open source SDKs with Amazon Bedrock to build business.</li><li>Optimize model responses by applying prompt engineering techniques.</li><li>Evaluate generative AI application components.</li><li>Implement responsible AI practices to protect generative AI.</li></ul>We recommend that attendees of this course have: <ul> <li>Completed the Generative AI Essentials AWS instructor-led course</li><li>Intermediate-level proficiency in Python</li><li>Familiarity with AWS Cloud</li></ul>This course is intended for: <ul> <li>Software developers.</li></ul><h4>Day 1</h4> Course Introduction Module 1: Exploring Components of Generative AI Applications on AWS <ul> <li>Understanding generative AI concepts</li><li>Identifying AWS generative AI stack components</li><li>Designing generative AI application components</li></ul>Module 2: Programming with Amazon Bedrock <ul> <li>Guiding model response generation</li><li>Using Amazon Bedrock programmatically</li></ul> Hands-on lab: Develop with Amazon Bedrock APIs Hands-on lab: Develop Streaming Patterns with Amazon Bedrock APIs Module 3: Applying Prompt Engineering for Developers <ul> <li>Introducing prompt engineering</li><li>Introducing prompt techniques</li><li>Optimizing prompts for better results</li></ul>Module 4: Using Amazon Bedrock APIs in Common Architectures <ul> <li>Implementing architecture patterns with Amazon Bedrock APIs</li><li>Exploring common use cases</li><li>Adding conversational memory to extend context</li></ul> Hands-on lab: Develop Conversation Patterns with Amazon Bedrock APIs[/b] Module 5: Customizing Generative AI Responses with RAG <ul> <li>Implementing Retrieval Augmented Generation (RAG)</li><li>Using Amazon Bedrock Knowledge Bases</li></ul> Hands-on lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases[/b] Module 6: Integrating Open Source Frameworks with Amazon Bedrock <ul> <li>Invoking a foundation model in Amazon Bedrock using LangChain</li><li>Using LangChain for context-aware responses</li></ul> Hands-on lab: Develop a Generative AI Application Pattern using Open Source Frameworks and Amazon Bedrock Knowledge Bases[/b] <h4>Day 2</h4> Module 7: Evaluating Generative AI Application Components <ul> <li>Evaluating application components</li><li>Evaluating model output</li><li>Evaluating RAG output</li><li>Optimizing latency and cost</li></ul> Hands-on lab: Evaluating Retrieval Augmented Generation (RAG) Applications[/b] Module 8: Implementing Responsible AI <ul> <li>Understanding responsible AI</li><li>Mitigating bias and addressing prompt misuses</li><li>Using Amazon Bedrock Guardrails</li></ul> Hands-on lab: Securing Generative AI Applications Using Bedrock Guardrails[/b] Module 9: Using Tools and Agents in Generative AI Applications <ul> <li>Using tools</li><li>Understanding AI agents</li><li>Understanding open source agentic frameworks</li><li>Understanding agent interoperability</li></ul>Module 10: Developing Amazon Bedrock Agents <ul> <li>Implementing Amazon Bedrock Flows</li><li>Designing Amazon Bedrock Agents</li><li>Developing Amazon Bedrock Inline Agents</li><li>Designing multi-agent collaboration</li><li>Using Amazon Bedrock AgentCore</li></ul> Hands-on lab: Developing Amazon Bedrock Agents Integrated with Amazon Bedrock Knowledge Bases and Guardrails Course Wrap-UpIn this course, you will learn to do the following: - Develop generative AI applications using Amazon Bedrock. - Design architecture patterns of generative AI applications. - Configure Amazon Bedrock APIs to invoke foundation models (FMs) programmatically. - Develop agentic AI applications by integrating Amazon Bedrock tools and open source frameworks. - Build custom solutions with Retrieval Augmented Generation (RAG) and Amazon Bedrock Knowledge Bases. - Integrate open source SDKs with Amazon Bedrock to build business. - Optimize model responses by applying prompt engineering techniques. - Evaluate generative AI application components. - Implement responsible AI practices to protect generative AI.We recommend that attendees of this course have: - Completed the Generative AI Essentials AWS instructor-led course - Intermediate-level proficiency in Python - Familiarity with AWS CloudThis course is intended for: - Software developers.Day 1 Course Introduction Module 1: Exploring Components of Generative AI Applications on AWS - Understanding generative AI concepts - Identifying AWS generative AI stack components - Designing generative AI application components Module 2: Programming with Amazon Bedrock - Guiding model response generation - Using Amazon Bedrock programmatically Hands-on lab: Develop with Amazon Bedrock APIs Hands-on lab: Develop Streaming Patterns with Amazon Bedrock APIs Module 3: Applying Prompt Engineering for Developers - Introducing prompt engineering - Introducing prompt techniques - Optimizing prompts for better results Module 4: Using Amazon Bedrock APIs in Common Architectures - Implementing architecture patterns with Amazon Bedrock APIs - Exploring common use cases - Adding conversational memory to extend context Hands-on lab: Develop Conversation Patterns with Amazon Bedrock APIs[/b] Module 5: Customizing Generative AI Responses with RAG - Implementing Retrieval Augmented Generation (RAG) - Using Amazon Bedrock Knowledge Bases Hands-on lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases[/b] Module 6: Integrating Open Source Frameworks with Amazon Bedrock - Invoking a foundation model in Amazon Bedrock using LangChain - Using LangChain for context-aware responses Hands-on lab: Develop a Generative AI Application Pattern using Open Source Frameworks and Amazon Bedrock Knowledge Bases[/b] Day 2 Module 7: Evaluating Generative AI Application Components - Evaluating application components - Evaluating model output - Evaluating RAG output - Optimizing latency and cost Hands-on lab: Evaluating Retrieval Augmented Generation (RAG) Applications[/b] Module 8: Implementing Responsible AI - Understanding responsible AI - Mitigating bias and addressing prompt misuses - Using Amazon Bedrock Guardrails Hands-on lab: Securing Generative AI Applications Using Bedrock Guardrails[/b] Module 9: Using Tools and Agents in Generative AI Applications - Using tools - Understanding AI agents - Understanding open source agentic frameworks - Understanding agent interoperability Module 10: Developing Amazon Bedrock Agents - Implementing Amazon Bedrock Flows - Designing Amazon Bedrock Agents - Developing Amazon Bedrock Inline Agents - Designing multi-agent collaboration - Using Amazon Bedrock AgentCore Hands-on lab: Developing Amazon Bedrock Agents Integrated with Amazon Bedrock Knowledge Bases and Guardrails Course Wrap-Up2 days1250.001250.001250.001250.00980.004000.001565.002035.001770.001390.001920.001325.001595.00