Tracking Model Training in Jupyter Notebooks with MLflow

When experimenting with machine learning models, keeping track of different hyperparameters, training runs, and evaluation metrics can quickly become chaotic. This text-only course guides you through organizing your machine learning workflow by integrating MLflow tracking directly into your Jupyter notebooks. By reading through structured explanations and practicing with written code examples, you will transform your unstructured experimental scripts into a reproducible, organized machine learning pipeline. You will gain the confidence to compare runs, manage model artifacts, and transition from local experiments to structured model registries. What you'll learn: Understand foundational MLOps concepts, key terminology, and the MLflow architecture; Configure MLflow tracking environments and initialize runs within Jupyter notebooks; Log parameters, custom metrics, and tags systematically during model training; Apply autologging features for popular machine learning libraries to simplify your code; Manage and version trained models using the MLflow Model Registry; Compare training runs to identify the best-performing model configurations. You will begin by learning core machine learning lifecycle concepts and setting up your tracking environment. From there, the text guides you through manual and automatic logging techniques, culminating in organizing your experiments and registering your final models. This course is designed for beginner data scientists, machine learning enthusiasts, and developers who are familiar with basic Python and want to organize their modeling workflows. No prior experience with MLflow or MLOps is required. Start reading today to bring structure, reproducibility, and clarity to your machine learning experiments.