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MLOps: Operationalizing Machine Learning at Scale

Learn MLOps practices for deploying, monitoring, and maintaining ML models in production environments.

Rottawhite Team15 min readDecember 23, 2024
MLOpsDevOpsML Deployment

What is MLOps?

MLOps applies DevOps principles to machine learning, enabling reliable, scalable, and automated ML systems.

The MLOps Lifecycle

  • **Data Management**
  • Collection
  • Versioning
  • Quality monitoring
  • **Model Development**
  • Experiment tracking
  • Version control
  • Reproducibility
  • **Deployment**
  • Packaging
  • Serving infrastructure
  • A/B testing
  • **Monitoring**
  • Performance tracking
  • Drift detection
  • Alerting
  • **Retraining**
  • Trigger conditions
  • Automated pipelines
  • Validation

    • Key MLOps Tools

    Experiment Tracking

  • MLflow
  • Weights & Biases
  • Neptune

    • Model Registry

  • MLflow Model Registry
  • DVC
  • Kubeflow

    • Orchestration

  • Airflow
  • Kubeflow Pipelines
  • Prefect

    • Serving

  • TensorFlow Serving
  • TorchServe
  • Seldon

    • Monitoring

  • Evidently
  • WhyLabs
  • Arize

    • Best Practices

    Reproducibility

  • Version everything
  • Containerize environments
  • Document experiments

    • Testing

  • Unit tests for code
  • Data validation
  • Model validation

    • Automation

  • CI/CD pipelines
  • Automated retraining
  • Deployment automation

    • Monitoring

  • Track model performance
  • Detect data drift
  • Set up alerts

    • Organizational Considerations

  • Cross-functional teams
  • Clear ownership
  • Documentation culture
  • Incident response

    • Conclusion

    MLOps is essential for reliable, scalable ML systems in production.

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