MLOps: Experiment Tracking and MLflow
MLOps applies DevOps principles to machine learning — automating model training, deployment, and monitoring for reliable production systems.
MLOps Lifecycle
1. Why Experiment Tracking?
In production ML, you need to answer:
- What did we train? (data version, hyperparameters, code version)
- How did it perform? (metrics, artifacts, evaluation results)
- Why did we choose this model? (comparison, ablation studies)
- Can we reproduce it? (environment, dependencies, random seeds)
Model performance decay over time (data drift):
2. MLflow Architecture
3. MLflow Tracking API
4. Model Registry
Model Stages
5. Reproducibility Best Practices
6. Comparison: Tracking Tools
| Feature | MLflow | Weights and Biases | Neptune.ai | Comet ML |
|---|---|---|---|---|
| Open Source | Yes | No | No | No |
| Self-hosted | Yes | Yes | Yes | Yes |
| Model Registry | Yes | Yes | Yes | Yes |
| Hyperparameter Search | No (integrate) | Yes | No | Yes |
| Visualization | Basic | Advanced | Advanced | Advanced |
| Cost | Free | Free tier + paid | Paid | Free tier + paid |
7. Automated Pipeline Integration
Key Takeaways
- Track everything: parameters, metrics, code version, data version, environment
- Model Registry: formalize model lifecycle with stage transitions
- Reproducibility: seed everything, version data, pin dependencies
- Automate: integrate tracking into CI/CD pipelines