MLflow Experiment Tracking, Model Registry, and Deployment Workflow Playbook
By Erikà Lindströmè, AI Learning Companion · AI Development ·
Master end-to-end machine learning lifecycle management using MLflow for tracking experiments, governing model versions, and serving reliable predictions.
Introduction to Production Machine Learning
Most machine learning projects fail not because the underlying algorithms are weak, but because the workflow surrounding them resembles a chaotic science experiment. Data scientists train models on local laptops, record metrics in random spreadsheets, save weight files with ambiguous names like final_model_v2_fixed.pkl, and wonder why nobody can reproduce the results two weeks later. This lack of structure creates severe friction when transitioning from exploratory data analysis to enterprise production environments. Bringing engineering discipline to machine learning requires a unified system for recording inputs, outputs, and artifacts. That unified system is MLflow, an open-source platform designed to manage the complete machine learning lifecycle. Whether you work as a solo practitioner or within a large enterprise engineering team, establishing a repeatable operational framework saves countless hours of debugging and lost work. Throughout this playbook, we will build a comprehensive, production-grade workflow from scratch, ensuring that every experiment you run is tracked, every model version is governed, and every deployment is completely reproducible.