Generative AI and Large Language Models on AWS

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Noah Gift

2:03:46

30 View

01 - Course introduction.mp4

00:55

02 - Cloud service model for AI.mp4

03:47

03 - Cloud deployment model for AI.mp4

03:35

04 - Benefits of cloud computing.mp4

03:02

05 - AWS cloud adoption framework for AI.mp4

02:46

06 - Development environment for AI.mp4

04:18

07 - MLOps challenges and opportunities with Python and Rust.mp4

08:18

08 - Generative AI workflow with Rust.mp4

05:50

09 - Python for data science in the era of Rust and generative AI.mp4

06:07

10 - Emerging Rust LLMOps workflows.mp4

03:57

11 - AWS CodeCatalyst for Rust.mp4

04:55

12 - SageMaker Code editor.mp4

03:43

13 - Lightsail for research.mp4

03:28

14 - Serverless Bedrock diagram.mp4

02:28

15 - Bedrock knowledge agent with retrieval-augmented generation (RAG).mp4

02:06

16 - Demo AWS Bedrock list with Rust.mp4

02:50

17 - Diagram Serverless Rust on AWS.mp4

02:21

18 - Diagram Rust Axum Greedy Coin microservice.mp4

03:14

19 - Demo Rust Axum Greedy Coin.mp4

03:40

20 - Demo Rust Axum Docker.mp4

04:42

21 - Diagram Prompt engineering.mp4

03:47

22 - Summarizing text with Claude.mp4

05:28

23 - AWS CodeWhisperer for Rust.mp4

07:47

24 - Installing and configuring CodeWhisperer.mp4

02:19

25 - Using CodeWhisperer CLI.mp4

04:28

26 - Building Bash functions.mp4

05:38

27 - Building a Bash CLI.mp4

03:13

28 - Key components of AWS Bedrock.mp4

03:11

29 - Getting started with the Bedrock SDK.mp4

02:57

30 - Cargo SDK for Rust Bedrock.mp4

01:25

31 - Bedrock Boto3 Listing models.mp4

02:03

32 - Rust Listing Bedrock models.mp4

01:57

33 - Invoking Claude with Bedrock.mp4

03:31

Description

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.

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Artificial Intelligence

Amazon AWS

Noah Gift

Instructor's Courses

Author of Practical MLOps, Enterprise MLOps, Developing on AWS with C#, Pragmatic AI, and Python for DevOps. Certified on Multiple MLOps certifications including Google-Professional Machine Learning Engineer and AWS Certified Machine Learning - Specialty. Adjunct Professor at Duke MIDS & Northwestern Graduate Data Science & AI. Held business roles including CTO, general manager, consulting CTO, and cloud architect. Consults with start-ups and other companies on machine learning and cloud architecture. AWS ML Hero, Python Software Foundation Fellow, AWS Subject Matter on Machine Learning, AWS Certified Solutions Architect, AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, Google Certified Professional Architect, and AWS Academy Accredited Instructor. Published books, videos, and courses on cloud machine learning, DevOps, Python, Data Science, Big Data and AI. ★ Specialties ★ ° Cloud-native Machine Learning and AI ° Directly teaching cutting edge skills to students that lead to jobs ° Creating world-class content in all forms ° Building Companies ° Shipping new Products ° Leading and growing engineering teams ° Production Machine Learning, Deep Learning, Big Data, and AI ° Serverless Data Engineering ° Advising Early Stage Startups/Consulting CTO services ° Distributed Systems and Scalability

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