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Learn to build generative AI solutions on AWS by working hands-on with Amazon Bedrock, Retrieval Augmented Generation pipelines, Amazon Q Developer, and open-source LLM toolchains. You will apply tokenization concepts to understand model pricing and context windows, construct RAG pipelines grounded in your own knowledge bases, and use the Bedrock SDK in Rust and Python to invoke foundation models programmatically. The course covers Amazon Q Developer for AI-assisted code generation, security scanning, and documentation workflows across VS Code and IntelliJ. You will compile llama.cpp with parallel build optimizations informed by Amdahl's Law, package models in the GGUF quantization format, and deploy open-source LLMs on AWS EC2 GPU instances. The course also introduces SageMaker Canvas for no-code visual machine learning and the UV Python packaging tool for dependency management. By completing this course, you will be able to evaluate trade-offs between managed AWS services, open-source toolchains, and no-code platforms for production generative AI workloads.
