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MLflow Tutorial: Tracking Experiments and Models
MLflow records what produced each model so you never have to remember. This tutorial trains three models on real bike-sharing data, logs every run, compares them by querying the tracking store, registers the best one, points a champion alias at it and loads it back by that alias, all on a laptop.
MLflow for Data Scientists: Tracking Experiments
Data scientists usually meet MLflow inside a notebook, where the useful habits are simple: log a baseline, group a sweep under one parent run, let autologging capture parameters, and compare everything as a DataFrame. This walkthrough runs that workflow on the UCI Wine Quality data with MLflow 3.16.
Experiment Tracking: MLflow vs Weights & Biases
MLflow and Weights & Biases are the two most common ways to track machine learning experiments. This comparison logs the same runs to both with almost identical code, shows where each keeps the data, and sets out the real differences: hosting, licence, collaboration, hyperparameter search and lock-in.
Model Versioning with MLflow and DVC
A model version is only useful if you can say what produced it. This guide combines DVC for data with MLflow for models, so each registered version records its data hash and Git commit, then rebuilds an old version from that record alone and checks that the predictions come out identical.