Databricks MLOps and LLMOps with MLflow and OpenAI

Moving machine learning and generative AI applications from initial experimentation into reliable production requires structured operational practices. This text-based guide introduces you to the core principles of MLOps and LLMOps using Databricks, MLflow, and OpenAI API integrations. You will build a solid understanding of how to orchestrate end-to-end data pipelines, manage experiment tracking, deploy models, and implement retrieval-augmented generation patterns with confidence. What you'll learn: - Understand foundational concepts of MLOps, LLMOps, and the Databricks ecosystem - Track machine learning parameters, metrics, and artifacts systematically using MLflow Tracking - Manage model versions and stage transitions using the MLflow Model Registry - Integrate OpenAI models into Databricks workflows for text generation and prompt workflows - Build foundational retrieval-augmented generation (RAG) pipelines with vector search concepts - Apply basic model evaluation and monitoring techniques to maintain pipeline reliability The course begins with foundational terminology, Databricks workspace fundamentals, and core concepts before progressing into practical code walkthroughs for experiment logging, model governance, and generative AI pipelines. This course is designed for aspiring data scientists, software developers, and data practitioners who want to understand machine learning operations from the ground up, with no prior MLOps experience required. Start reading today to learn how modern machine learning and LLM applications are managed in production.