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.
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Pragmatic AI Labs is in Tier 4: commercial training companies and platform-made courses of our institution ranking (58/100).
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Instructor: Dennis Freeman, Kendra Pugh This course provides an integrated introduction to electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making. The lecture videos provide an overview of each topic, whi…
This course explores the world of Transformers and Large Language Models (LLMs). You will learn the evolution of NLP methods, the core components of the Transformer architecture, along with how they relate to LLMs as well as techniques to enhance model performance for real-world applications. Throu…
Dive into the intriguing world of AI Transformers with this curated YouTube playlist. Featuring expert insights, educational content from leading organizations, and engaging discussions, this playlist keeps you informed about the latest breakthroughs in Transformer technology.
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Harvard University · edX
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
