Deploying ML Models with MLOps
What happens after training — shipping, monitoring, and maintaining models in production.
Inspired by learning material from Microsoft
Training a model is only half the battle. MLOps is the practice of reliably running ML in production.
The challenge
Unlike regular software, ML systems depend on data that changes over time. A model that works today can quietly degrade tomorrow.
Core MLOps practices
- Versioning: track data, code, and model versions together.
- Automated pipelines: reproducible training and deployment.
- Continuous monitoring: watch accuracy, latency, and inputs.
- Drift detection: alert when incoming data shifts from training data.
Retraining
When performance drops, automated pipelines can retrain and redeploy with minimal manual effort.
Governance
Document models, track lineage, and ensure compliance — especially in regulated industries.
Key takeaways
- MLOps keeps models reliable in production.
- Data drift makes monitoring essential.
- Automation and versioning enable safe, repeatable updates.