TrainingDevelopment & DevOps / AWS DeveloperAWS-DGAI
AWS-DGAIAWS · authorised training
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.
- Duration
- 2 days
- Level
- Advanced
- Provider
- AWS
- Topic
- Development & DevOps / AWS Developer
Course outline
- Overview of ML
- Basics of generative AI
- Generative AI use cases
- Generative AI in practice
- Risks and benefits
- 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
- • 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
- • 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
- • 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
- • 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
- • 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
Skills you will gain
- 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
Who should attend
- This course is intended for Software developers interested in leveraging large language models without fine-tuning
Prerequisites
- AWS Technical Essentials
- Intermediate-level proficiency in Python
5000 PLN
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