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