MLOps Fundamentals with MLflow, Docker, and PySpark

Managing the end-to-end lifecycle of machine learning models requires robust tools for tracking, reproducibility, and deployment. This text-based guide introduces you to foundational MLOps practices, giving you the knowledge to organize experiments and standardize model deployments. You will read clear explanations and examine practical code examples that demonstrate how to manage models from training through registry and deployment. By understanding how MLflow integrates with Docker and PySpark, you will gain confidence in structuring scalable machine learning pipelines. What you'll learn: - Understand core MLOps terminology, lifecycle stages, and model tracking fundamentals - Track machine learning parameters, metrics, and artifacts using MLflow Tracking - Package reproducible code environments with MLflow Projects and Docker containers - Manage versioning, stages, and governance using the MLflow Model Registry - Configure scalable data pipelines and distributed training workflows with PySpark - Apply modern best practices for model packaging and containerized deployment The course begins with foundational concepts and definitions of the machine learning operations lifecycle before progressing through structured text modules covering experiment tracking, containerization, and distributed execution patterns. This course is designed for aspiring machine learning engineers, data scientists, and developers looking to understand MLOps fundamentals without prior operational experience. Start reading today to establish clean, reproducible machine learning workflows in your projects.