Who is MLOps and Why It Matters

MLOps (Machine Learning Operations) is a discipline that combines machine learning (ML), development, and operations.
If DevOps automates and speeds up software delivery, MLOps does the same but for machine learning models.

What Does an MLOps Engineer Do?

The role of an MLOps engineer is to ensure that ML models are not only trained but also reliably deployed, monitored, and maintained in production.

Key Responsibilities:

  • Automating ML pipelines: training, testing, and deployment (CI/CD → CI/CD/CT).
  • Data and feature validation (Data Quality, Feature Store).
  • Monitoring model metrics (accuracy, data drift, performance degradation).
  • Model versioning and governance (MLflow, DVC, Model Registry).
  • Scaling inference (Kubernetes, Kubeflow, Seldon).
  • Bridging the gap between Data Science and DevOps teams.

How MLOps Differs From DevOps

  • DevOps → focuses on code and applications.
  • MLOps → focuses on data, models, and ML-driven services.
  • MLOps addresses unique challenges like data drift, continuous retraining, and experiment tracking.

Example MLOps Practices

  • Automatically retraining models when new data arrives.
  • Deploying ML models with Kubernetes + MLflow.
  • Setting alerts when model performance drops in production.

Why It Matters

  • Makes ML projects predictable and repeatable.
  • Reduces risks caused by poor data quality or model drift.
  • Accelerates the path from experiment to production.

Conclusion

MLOps is essentially DevOps for machine learning.
It ensures ML models become a reliable part of the product lifecycle, not just research experiments.