MLflow Tutorial: How to Track ML Experiments Like a Pro (2026)

This article provides a comprehensive guide on how to get started with MLflow for experiment tracking in machine learning projects. Here's a summary of the key points and steps:

1. Introduction to MLflow:

   • MLflow is an open-source platform designed to streamline the end-to-end machine learning lifecycle.
   • It helps track experiments, manage models, and deploy them.

2. Setting Up MLflow Locally:

   • Install MLflow using pip install mlflow.
   • Start a local tracking server with mlflow ui.

3. Basic Experiment Tracking:

   • Use mlflow.start_run() to begin an experiment.
   • Log parameters and metrics using mlflow.log_param(key, value) and mlflow.log_metric(metric_name, metric_value).
   • Save the model using mlflow.sklearn.save_model(model, "model_path").

4. Example Code:

   python
    
   1import mlflow
   2from sklearn.datasets import load_iris
   3from sklearn.linear_model import LogisticRegression
   4
   5# Load data and train a model
   6iris = load_iris()
   7X = iris.data
   8y = iris.target
   9model = LogisticRegression

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