Databricks for ML, MLOps, and Gen AI

Databricks for Machine Learning, MLOps, and Generative AI is an expert-level course designed for learners who want to build, deploy, govern, and operate production-grade AI systems on the Databricks Lakehouse. This course goes beyond basic notebook-based model training. Learners will understand how to prepare ML-ready Delta datasets, engineer point-in-time correct features, track and reproduce experiments with MLflow, register and promote models safely, and deploy both batch and real-time inference pipelines. The course also covers production monitoring, drift detection, model serving, CI/CD, cost optimization, governance, and responsible AI controls. A major focus of the course is modern Generative AI and LLMOps on Databricks. Learners will explore enterprise RAG architecture, Mosaic AI Vector Search, prompt evaluation, retrieval quality, grounding, citations, safety, observability, and feedback loops. By the end of the course, learners will be able to design and operate reliable AI workflows that are secure, auditable, scalable, and business-ready. This course is ideal for data scientists, ML engineers, AI engineers, MLOps engineers, GenAI practitioners, platform engineers, and technical leaders who want to master Databricks as a unified platform for machine learning, MLOps, and Generative AI.