MLOps: Deploying and operating AI models in production

MLOps is the set of practices combining DevOps, Data Engineering and Machine Learning to move models from experimentation to production reliably and repeatably.

Why MLOps? Gartner research shows more than 50% of AI projects never reach production. The main cause is not weak models but missing operational discipline: a model that works in a notebook degrades on real data, with no monitoring and no easy rollback.

Standard MLOps pipeline

1. Data versioning

• Version training data (DVC, LakeFS) for reproducibility
• Track data drift: has the new data distribution shifted from training?
• Reproducible data pipelines

2. Training & experimentation

• Track experiments: log hyperparameters, metrics, model artifacts (MLflow, Weights & Biases)
• CI for ML: every code change reruns the pipeline and compares metrics
• Model registry: store versions, stages (staging/production), metadata

3. Deployment

• Serving: REST API (FastAPI, BentoML), batch inference, or edge (ONNX, TensorRT)
• Rollout strategy: canary, shadow deployment (parallel, no user impact)
• A/B test new vs old model before full swap

4. Monitoring

• Performance drift: business metrics (conversion, accuracy) dropping over time
• Data drift: input distribution changes
• Concept drift: input-output relation changes (customer behavior shifts)
• Alert when drift crosses a threshold to trigger retraining

5. Continuous training

• Scheduled or drift-triggered retraining
• Validate new model before promotion
• Auto rollback if the new model underperforms the baseline

Common mistakes

• Monitoring only infra health (CPU, latency) and ignoring model quality
• No baseline to compare against after retraining
• Shipping to production without a rollback strategy
• Ignoring data drift until business metrics drop

Reference stack

• Experiments & registry: MLflow, W&B
• Orchestration: Kubeflow, Airflow, Prefect
• Serving: BentoML, Seldon, Triton
• Monitoring: Evidently, Arize, WhyLabs

Conclusion: MLOps is not an add-on after the model is done — it is an inseparable part of a production AI system. Investing early avoids expensive "AI technical debt" and shortens time-to-market for every new model.