MLflow for Machine Learning Experiment Tracking
🧪 MLflow for Machine Learning Experiment Tracking
📌 What is MLflow?
MLflow is an open-source platform to manage the complete machine learning lifecycle, including:
Experiment tracking
Model training
Model packaging
Model deployment
You can use it with any ML library (e.g., scikit-learn, PyTorch, TensorFlow).
✅ Why Use MLflow?
Track experiments automatically (metrics, hyperparameters, artifacts)
Compare model runs easily
Log models and version them
Share and deploy models from a central interface
🛠️ Key Components of MLflow
Component Description
mlflow.tracking Logs metrics, parameters, and artifacts
mlflow.projects Run reproducible ML code
mlflow.models Log and serve models
mlflow.registry Model versioning and lifecycle management
🔧 Installation
pip install mlflow
You can also launch the MLflow UI locally:
mlflow ui
Then go to: http://localhost:5000
🚀 Quick Example (with Scikit-learn)
import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
# Start experiment
with mlflow.start_run():
# Model and params
clf = RandomForestClassifier(n_estimators=100, max_depth=3)
clf.fit(X_train, y_train)
# Predictions
preds = clf.predict(X_test)
acc = accuracy_score(y_test, preds)
# Log parameters and metrics
mlflow.log_param("n_estimators", 100)
mlflow.log_param("max_depth", 3)
mlflow.log_metric("accuracy", acc)
# Log model
mlflow.sklearn.log_model(clf, "random_forest_model")
After running this, open the MLflow UI to see:
Parameters
Metrics
Saved models
Artifacts
📁 Artifacts You Can Log
Model files (e.g., .pkl, .pt)
Plots (e.g., confusion matrix)
Training logs
Feature importance charts
mlflow.log_artifact("confusion_matrix.png")
🧠 Tips for Using MLflow
Combine with notebooks, scripts, or pipelines
Use tags to organize runs (e.g., model type, experiment name)
Integrate with Docker, AWS S3, or Databricks
Use mlflow.set_experiment("experiment_name") to organize runs under one project
🌐 Serving a Model with MLflow
You can serve your model as a REST API:
mlflow models serve -m runs:/<run-id>/random_forest_model -p 1234
Then send POST requests to http://localhost:1234/invocations with input data.
📦 Model Registry (Advanced)
MLflow has a built-in Model Registry to:
Register models
Track versions
Move models through stages (Staging → Production → Archived)
mlflow.register_model("runs:/<run-id>/model", "MyModelName")
🔗 Integrations
Platform Integration
scikit-learn Native support
TensorFlow / Keras mlflow.tensorflow and mlflow.keras
PyTorch Custom logging or use mlflow.pytorch
XGBoost / LightGBM Supported directly
Airflow / Prefect Use MLflow to log experiments in pipelines
AWS / Azure / GCP Save models/artifacts to cloud storage
📚 Learn More
Official Docs: https://mlflow.org
GitHub Repo: https://github.com/mlflow/mlflow
Tutorial Video: MLflow Crash Course (YouTube)
🎯 Final Thoughts
MLflow helps you:
Stay organized
Track all your experiments
Collaborate in teams
Reproduce and deploy models easily
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