This comprehensive MLOps course with Databricks provides a step-by-step guide to building, deploying, and monitoring machine learning models in production environments. The course begins with an introduction to MLOps concepts and Databricks as a platform for collaborative ML development. Learners explore developing models in Databricks, integrating MLflow for experiment tracking, logging, and model registration. The course dives into model serving architectures, deploying model endpoints, and managing asset bundles efficiently. Participants also learn CI/CD strategies and deployment best practices to ensure robust, reproducible pipelines. Monitoring ML models in production is covered, teaching learners how to track performance, detect drift, and optimize models over time. Practical examples and hands-on exercises help participants understand how to implement MLOps workflows end-to-end, from development to production. By the end of the course, learners will be equipped to confidently develop ML models, deploy them using Databricks, manage CI/CD pipelines, and monitor performance, ensuring production-ready machine learning systems. This course is ideal for data scientists, ML engineers, and AI practitioners aiming to operationalize machine learning effectively.