MLOps and Machine Learning with Databricks REST API

Deploying and managing machine learning models at scale requires structured workflows and robust automation. This text-based course guides you through the fundamentals of machine learning operations (MLOps) using Databricks and its REST API 2.0. You will build a solid understanding of how to automate workspace tasks, manage compute resources, track machine learning experiments, and programmatically control end-to-end model lifecycles. What you'll learn: - Understand core MLOps terminology, model lifecycle concepts, and API fundamentals - Configure authentication and send structured requests to the Databricks REST API - Automate model training jobs and experiment tracking with MLflow integrations - Manage compute clusters and workspace resources programmatically - Implement CI/CD patterns and model registry workflows for automated deployment - Monitor deployed model endpoints for performance and operational stability The course begins with foundational concepts and essential terminology before guiding you step-by-step through practical written tutorials and API code examples. This course is designed for aspiring data engineers, machine learning engineers, and developers who want a practical entry point into MLOps automation without prior API management experience. Start reading today to automate your machine learning workflows with confidence.