Overview

This immersive, hands-on course spans three weeks and introduces participants to the design and development of modern machine learning platforms, emphasizing the deployment and operationalization of generative AI systems. It provides comprehensive guidance on every stage of the machine learning lifecycle—from project scoping and exploratory data analysis to feature engineering, model training, and selection, as well as production workflows, monitoring, and MLOps/LLMOps practices.

Participants will gain practical expertise working with essential components of modern ML platforms, including feature stores, model registries, ML pipelines, monitoring systems, and CI/CD automation for machine learning workflows. The course will also explore key architectures for generative AI, such as Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), Agent Connect Protocol (ACP), and other generative AI paradigms.

The course focuses on giving hands-on experience in designing modern generative AI and learning emerging architectures like RAG, MCP, ACP, and others. The course provides a foundation for understanding and implementing modern approaches for generative AI solution productization with MLOps and LLMOps.

As the course main deliverable, you’re required to implement a capstone project. The capstone project will integrate critical elements of MLOps and LLMOps, such as reproducible data and ML pipelines, scalable data processing, experiment tracking, observability, model registries, and event-driven workflows. You will emerge equipped to efficiently design, deploy, and manage generative AI solutions in real-world contexts.