MLOps — running models in production

MLOps (Machine Learning Operations) is the set of practices for deploying, monitoring and maintaining machine learning models in production. It is to ML what DevOps is to software — the engineering discipline that turns a working prototype into a reliable service.

Most ML models that work in a notebook never make it to production. MLOps is the bridge. It covers everything from packaging a model for deployment to detecting when it starts giving bad predictions months after launch.

Traditional software is deterministic — the same input gives the same output. ML systems are probabilistic and their performance degrades over time as the real world changes (data drift). A fraud detection model trained on 2024 data will perform worse in 2026 because fraud patterns evolve.

This means ML systems need monitoring that traditional software does not: tracking prediction quality, detecting data drift, triggering retraining, and managing multiple model versions simultaneously.

A production ML system typically includes: a feature store (consistent features for training and serving), a model registry (versioned model storage), a training pipeline (automated model retraining), a serving layer (API for predictions), a monitoring system (tracking performance and drift), and CI/CD for models (automated testing and deployment).

You do not need all of this on day one. Start with a simple serving layer and monitoring, then add components as complexity grows.

MLOps engineering is a natural career switch for DevOps, backend and platform engineers. The skills transfer directly — Docker, Kubernetes, CI/CD, cloud infrastructure, monitoring. You add ML-specific knowledge (model serving, drift detection, feature engineering) on top of your existing platform skills.

Demand in India is growing fast. Companies that have deployed ML models (fintech, e-commerce, logistics, SaaS) all need people to keep those models running reliably.

You do not need to understand ML algorithms deeply to do MLOps — you need to understand how models are trained, what inputs they need, and how to serve predictions. Start by deploying a simple model as an API using FastAPI or Flask, containerise it with Docker, and set up basic monitoring. Then learn about model registries and automated retraining.