Production Machine Learning with Azure Databricks

Moving machine learning models from a local notebook to a reliable production environment is one of the biggest challenges in data science today. This text-based course guides you through the essential concepts of machine learning operations (MLOps) using Azure Databricks. You will transition from building isolated models to establishing automated, reproducible, and monitored production pipelines, ensuring your models remain accurate and reliable over time. What you'll learn: - Understand foundational MLOps principles and the production lifecycle of machine learning models. - Track experiments, parameters, and metrics using integrated MLflow tracking. - Register, version, and manage models in the centralized Azure Databricks Model Registry. - Deploy trained models as real-time API endpoints or batch scoring pipelines. - Monitor model performance and identify data drift to maintain prediction quality. - Apply modern collaboration workflows and basic CI/CD concepts to machine learning projects. The course begins with core definitions and architectural basics before moving into practical configuration scenarios. You will read through detailed explanations, clear code snippets, and structured deployment workflows. This course is designed for aspiring ML engineers, data scientists, and developers who are new to production deployment and want to master Azure Databricks. No prior production experience is required. Start reading today to build reliable, production-ready machine learning pipelines.