Machine Learning with Databricks and MLflow

Streamlining the machine learning lifecycle is one of the most valuable skills in modern data science. This text-based course guides you through managing, tracking, and deploying machine learning models using the powerful combination of Databricks and MLflow. You will transition from writing isolated machine learning code to building structured, reproducible pipelines. By understanding the core mechanics of experiment tracking, model packaging, and deployment, you will gain the confidence to manage real-world machine learning lifecycles. What you'll learn: - Understand the foundational architecture of Databricks and how it integrates with MLflow. - Track parameters, metrics, and artifacts during model training to ensure complete reproducibility. - Manage the model lifecycle using the MLflow Model Registry to transition models from development to production. - Deploy trained models as scalable REST APIs and batch inference pipelines. - Apply modern MLOps best practices to monitor and maintain model performance over time. The course begins with essential concepts of workspace setup and data preparation, before moving step-by-step through experiment tracking, model registration, and deployment strategies. You will learn through clear written explanations, structured code walkthroughs, and conceptual explanations. This course is designed for beginner data scientists, software engineers, and data analysts who want to learn machine learning operations. No prior experience with Databricks or MLflow is required, though a basic understanding of Python is helpful. Start your journey into streamlined machine learning engineering today.