Practical Machine Learning on Databricks

Machine learning teams today must build scalable, production-ready workflows that move seamlessly from experimentation to deployment and monitoring. This course provides a practical introduction to machine learning on Databricks, helping professionals leverage modern tools such as AutoML, MLflow, Feature Store, and serverless deployment to streamline ML operations and accelerate business outcomes. Through hands-on examples and real-world scenarios, you will learn how to create baseline models, manage feature engineering workflows, automate ML pipelines, and deploy models efficiently using Databricks. The course also explores model versioning, workflow orchestration, CI/CD automation, and model drift detection to help you maintain reliable and scalable machine learning systems in production environments. What sets this course apart is its strong focus on practical implementation using Databricks-native tools and workflows. By combining foundational ML concepts with enterprise-ready deployment and automation strategies, the course prepares you to tackle modern MLOps and machine learning engineering challenges with confidence. This course is ideal for data scientists, ML engineers, data engineers, and developers looking to transition to Databricks-based machine learning workflows. Learners should have prior experience with Python, machine learning concepts, and familiarity with Apache Spark fundamentals.