Course

Developing Generative AI Applications on AWS – Intensive Training («AWSD03»)

This course is designed to introduce generative artificial intelligence (AI) to software developers interested in using large language models (LLMs) without fine-tuning.
Duration 2 days
Price 1'800.–
Labs: This course now also includes hands-on labs

Course facts

  • Describing generative AI and how it aligns to machine learning
  • Defining the importance of generative AI and explain its potential risks and benefits
  • Identifying business value from generative AI use cases
  • Discussing the technical foundations and key terminology for generative AI
  • Explaining the steps for planning a generative AI project
  • Identifying some of the risks and mitigations when using generative AI
  • Understanding how Amazon Bedrock works
  • Familiarizing yourself with basic concepts of Amazon Bedrock
  • Recognizing the benefits of Amazon Bedrock
  • Listing typical use cases for Amazon Bedrock
  • Describing the typical architecture associated with an Amazon Bedrock solution
  • Understanding the cost structure of Amazon Bedrock
  • Implementing a demonstration of Amazon Bedrock in the AWS Management Console
  • Defining prompt engineering and apply general best practices when interacting with foundation models (FMs)
  • Identifying the basic types of prompt techniques, including zero-shot and few-shot learning
  • Applying advanced prompt techniques when necessary for your use case
  • Identifying which prompt techniques are best suited for specific models
  • Identifying potential prompt misuses
  • Analyzing potential bias in FM responses and design prompts that mitigate that bias
  • Identifying the components of a generative AI application and how to customize an FM
  • Describing Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs
  • Identifying Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications
  • Describing how to integrate LangChain with LLMs, prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock
  • Describing architecture patterns that you can implement with Amazon Bedrock for building generative AI applications
  • Applying the concepts to build and test sample use cases that use the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach
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.

Day 1
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
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
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
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
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
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
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
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
This course includes presentations, demonstrations, and group exercises. Software developers interested in using LLMs without fine-tuning
  • Some exposure to Python
  • Attendance of the following courses or equivalent knowledge:
This course can be used as preparation for the following official AWS Certification: AWS Certified Machine Learning – Specialty

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