Generative AI and Large Language Models on AWS

سرفصل های دوره

If you’re looking for new ways to leverage generative AI and large language models (LLMs), AWS has every service you need. In this practical, hands-on course, instructor Noah Gift demonstrates the core concepts and technical skills required to set up cloud-based Rust development environments; construct serverless workflows with AWS Lambda and Step Functions; utilize AWS CodeCatalyst, SageMaker, and Lightsail; generate, edit, and summarize text and code with models like Claude and CodeWhisperer; and orchestrate models and data workflows using Amazon Bedrock. Along the way, test out your new skills with real-world coding projects designed to help you operationalize LLMs for machine learning applications.

01 - Course introduction

02 - Cloud service model for AI

03 - Cloud deployment model for AI

04 - Benefits of cloud computing

05 - AWS cloud adoption framework for AI

06 - Development environment for AI

07 - MLOps challenges and opportunities with Python and Rust

08 - Generative AI workflow with Rust

09 - Python for data science in the era of Rust and generative AI

10 - Emerging Rust LLMOps workflows

11 - AWS CodeCatalyst for Rust

12 - SageMaker Code editor

13 - Lightsail for research

14 - Serverless Bedrock diagram

15 - Bedrock knowledge agent with retrieval-augmented generation (RAG)

16 - Demo AWS Bedrock list with Rust

17 - Diagram Serverless Rust on AWS

18 - Diagram Rust Axum Greedy Coin microservice

19 - Demo Rust Axum Greedy Coin

20 - Demo Rust Axum Docker

21 - Diagram Prompt engineering

22 - Summarizing text with Claude

23 - AWS CodeWhisperer for Rust

24 - Installing and configuring CodeWhisperer

25 - Using CodeWhisperer CLI

26 - Building Bash functions

27 - Building a Bash CLI

28 - Key components of AWS Bedrock

29 - Getting started with the Bedrock SDK

30 - Cargo SDK for Rust Bedrock

31 - Bedrock Boto3 Listing models

32 - Rust Listing Bedrock models

33 - Invoking Claude with Bedrock

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تولید کننده: LinkedIn Learning (Lynda)