What Will You Learn?
In this course, you will learn to:
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
About This Course
Provider: AWS Training and Certificate
Format: Online
Duration: 4 hours to complete [Approx]
Target Audience: Advanced
Learning Objectives: Able to do programming in Python after completing this free course
Course Prerequisites: Intermediate to expert-level proficiency with Python programming language, AWS Technical Essentials, Practical Data Science with Amazon SageMaker (intermediate), Amazon Bedrock Getting Started (Fundamental), Foundations of Prompt Engineering (Intermediate)
Assessment and Certification: NA
Instructor: AWS Training and Certificate
Key Topics: Amazon Bedrock, Generative AI, LangChain, AWS, LLM
Topic Covered:
- - Introduction to Amazon Bedrock
- - Building Generative AI Applications on Amazon Bedrock
- - Applications and Use Cases
- - Application Components
- - Overview of Generative AI Application Components
- - Foundation Models and the FM Interface
- - Working with Datasets and Embeddings
- - Additional Application Components
- - RAG
- - Model Fine-Tuning
- - Securing Generative AI Applications
- - Generative AI Application Architecture
- - Foundation Models
- - Introduction to Amazon Bedrock Foundation Models
- - Using Amazon Bedrock FMs for Inference
- - Amazon Bedrock Methods
- - Data Protection and Auditability
- - Using 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
- - Architecture Patterns
- - Introduction to Architecture Patterns
- - Test Generation and Text Summarization
- - Question Answering
- - Chatbots
- - Code Generation
- - LangChain and Amazon Bedrock Agents
- - Hands-on Labs
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