What is MLOps, and how is it different from traditional DevOps?

What the interviewer wants to hear

• Practices to build, deploy and maintain ML systems reliably
• ML adds data and models as changing parts, not only code
• Needs experiment tracking, data versioning, model registry
• Models degrade over time, so monitoring and retraining are needed

Model answer

MLOps is a set of practices and tools for taking machine learning models into production and keeping them working. It builds on DevOps ideas like automation, CI/CD, infrastructure as code and monitoring. The difference is that an ML system has three moving parts, not one: code, data and the trained model. A model can break even when the code has not changed, because the incoming data changes. So MLOps adds experiment tracking to compare runs, data versioning for reproducibility, a model registry to manage versions and approvals, and pipelines that retrain and evaluate models automatically. Testing also covers data quality and model quality, not just unit tests. Finally, monitoring includes drift and prediction quality, not only uptime and latency, with a process to retrain when performance falls.

More easy MLOps Engineer questions

• Why does a team need a model registry? What information should it store?
• How do you make a model training run reproducible?
• How would you deploy a trained scikit-learn model as a REST API?
• What would you monitor for a model running in production?
• What is training-serving skew, and how do you prevent it?
• How do you decide between batch predictions and a real-time model API?
• Explain the difference between data drift and concept drift, and how you would detect each.
• What makes a good Docker image for a model-serving service?
• Which Kubernetes concepts matter most when you deploy a model service, and why?