MLOps Fundamentals with MLflow and Docker

Moving machine learning models from experimentation into reliable production environments requires robust MLOps practices. This text-based course guides you through orchestrating the full machine learning lifecycle using MLflow and Docker. You will build a solid understanding of experiment tracking, model registry workflows, and containerized deployment pipelines without unnecessary complexity. What you'll learn: - Understand core MLOps principles, terminology, and lifecycle management workflows - Track machine learning parameters, metrics, and artifacts using MLflow Tracking - Package reproducible code and dependencies using MLflow Projects - Containerize machine learning environments with Docker for consistent execution - Manage model versioning, staging, and lifecycle transitions in the MLflow Model Registry - Apply modern automation and reproducibility best practices across data science pipelines The course begins with foundational concepts and essential terminology before progressing into step-by-step written guides and code configurations for managing and containerizing your ML workflows. This course is designed for aspiring ML engineers, data scientists, and developers looking to establish structured MLOps workflows. No prior DevOps experience is required. Start reading today to streamline your machine learning development and deployment workflow.