A model that shows 92% accuracy in a notebook produces no value for the organisation until it goes live. The biggest pain for data science teams is exactly here: the road from model to API, and from API to sustainable operations. MLOps is the discipline that maps this road.
The Five Layers of MLOps
- Feature Store: differences between how a feature is computed at training versus serving time (training-serving skew) are the most common model killer. Tools like Feast and Databricks Feature Store guarantee consistency.
- Model Registry: which model version was trained on which dataset, with which hyperparameters, and with what performance? MLflow, Weights and Biases, Azure ML registry hold this inventory.
- CI/CD for Models: like code, models must be testable and deployable. Unit tests (data schema), integration tests (pipeline), canary deployment (traffic splitting).
- Monitoring: in production, model quality can drift (data drift, concept drift). Monitoring prediction distribution, feature distribution and business-KPI anomalies is essential.
- Retraining Pipeline: a model is not a one-time artefact but an asset continuously updated via feedback loops. Retraining conditions must be defined in advance (on performance drop? periodically? on new data?).
Maturity Levels
MLOps maturity is typically assessed against Google's three-stage model:
- Level 0: manual model deploy. Data scientists copy notebooks to production. Fine for small teams; doesn't scale.
- Level 1: automated training pipeline with manual deploy.
- Level 2: CI/CD with automated training, deploy and monitoring. Retraining is triggered through the feedback loop.
Most Turkish enterprises sit between Level 0 and Level 1. Reaching Level 2 requires a focused 9-12 month investment.
Tool Choices
- Tracking: MLflow (open source, widely used), W&B (strong team UX), Azure ML (Azure-native).
- Pipeline: Kubeflow (Kubernetes-native), Airflow (data + ML hybrid), Prefect (Python-native).
- Serving: Seldon, BentoML, KServe, Triton (GPU-heavy).
- Monitoring: Arize, WhyLabs, Evidently (open source).
A Recommended Phased Approach
At a Turkish banking client, the MLOps project progressed in a compact sequence: first moving two pilot models to MLflow, then adding CI/CD with Azure DevOps pipelines, then drift monitoring with Evidently, ending with a 15-model catalogue. The nine-month engagement tripled data-science productivity and cut model-operations cost by 60%.
