MLOps for Enterprise: Production ML Playbook 2026

Most ML projects reach a production POC and then rot. MLOps is the discipline that keeps models useful over years — retraining as data shifts, monitoring for drift, versioning models properly, and integrating cleanly with the rest of the platform. Here is what an enterprise MLOps stack looks like in 2026.

The MLOps Stack

1. Data + feature store — Feast, Tecton, Databricks Feature Store. Single source of truth for features.
2. Experiment tracking — MLflow, Weights & Biases, Neptune.
3. Model registry — MLflow, SageMaker Model Registry, Vertex AI Model Registry.
4. Orchestration — Airflow, Prefect, Dagster, Kubeflow.
5. Serving — SageMaker, Vertex AI, KServe, BentoML.
6. Monitoring — Evidently, Arize, Fiddler, WhyLabs.
7. Retraining pipelines — triggered by drift, performance, or schedule.

The Two Failure Modes

Model drift: data distribution changes; model quality silently degrades.
Feature drift: upstream data changes; features you assumed change meaning.
Both are silent killers. Without monitoring, you'll notice only when a business metric moves and someone traces it back to a rotting model 3-6 months later.

Cost Bands

Foundational MLOps: ₹15-45 lakh / $18K-54K one-time + $500-3K/month ongoing.
Full MLOps platform: ₹60 lakh - 1.5 Cr / $72K-180K one-time + $2K-15K/month.
Enterprise MLOps at scale: ₹1.5-5 Cr / $180K-600K + $15K-100K/month.

Build vs Buy

Cloud-native platforms: AWS SageMaker, Google Vertex AI, Azure ML. Fastest to production if you're deep in one cloud.
Best-of-breed: MLflow + Feast + Evidently + Airflow. More flexible, more operational overhead.
Databricks: Best if your data lake is already there.
Most enterprises: hybrid.

Regional Notes

India: Talent depth exceptional; MLOps is a common consulting engagement.
UAE: Growing rapidly; banking + government leading.
USA / UK / Australia: Most mature markets; enterprise MLOps now table-stakes.