What is the difference between MLOps and DevOps?

DevOps and MLOps both automate the software delivery lifecycle, but MLOps extends those practices to handle the unique demands of machine learning systems. DevOps manages source code, builds, and deployments. MLOps adds data versioning, feature stores, model training reproducibility, model registries, and continuous monitoring for drift in production models.

Definitions

DevOps is a set of practices and tools that unify development and operations. It centres on version control, automated testing, continuous integration and continuous delivery (CI/CD), infrastructure as code, and observability. The unit of delivery is application code packaged as binaries or container images.

MLOps is the application of DevOps principles to machine learning workflows. The unit of delivery is a trained model plus the data, features, and code that produced it. Reproducing a model requires capturing the dataset snapshot, the feature transformations, the hyperparameters, the training environment, and the resulting weights. MLOps tooling makes this reproducibility automatic.

DevOps vs MLOps at a glance

What MLOps adds on top of DevOps

Three additions matter most. First, data and feature versioning. The same model code can produce wildly different models depending on the training data. Tools like DVC, LakeFS, and feature stores such as Feast track those inputs so that any model can be rebuilt deterministically. Second, model registries. A registry stores model artefacts with metadata such as training run, metrics, owner, and approval status. SageMaker Model Registry, Vertex AI Model Registry, and MLflow are common choices. Third, production monitoring beyond infrastructure. A model can be serving fast, low-latency predictions while silently producing worse outcomes because the input distribution has shifted. MLOps platforms compare live data and predictions against training baselines and trigger alerts or retraining when drift crosses a threshold.

Practical guidance for Indian teams

Indian enterprises and Global Capability Centres are building MLOps platforms to support BFSI risk models, retail recommendation engines, and telecom churn prediction. Start by treating models as products with owners, SLAs, and a defined lifecycle. Pick one model registry and one experiment tracking tool rather than letting each team adopt its own. Use the same CI/CD platform that already serves your DevOps teams, and extend it with ML-specific stages such as data validation and model evaluation. Invest in feature stores only when multiple teams need to share features across models.

If you are early in the journey, learn how MLOps works in general, and how MLOps adoption looks in India specifically. The same teams will often deliver both classical ML pipelines and emerging generative AI applications, so plan your platform to handle both.

How Opsio helps

Opsio builds production MLOps platforms on AWS, Azure, and Google Cloud using SageMaker, Vertex AI, Azure Machine Learning, MLflow, and Kubeflow. Our AI and machine learning services cover data pipelines, feature stores, training automation, model deployment patterns, drift monitoring, and governance so your data science teams ship models to production reliably.

Frequently Asked Questions

Do I need MLOps if I only have a few models?

If you run two or three models that change rarely, a lightweight setup with version control and notebooks may be enough. As soon as you need to retrain on a schedule, roll back a bad model, or audit which dataset produced a prediction, you have outgrown ad-hoc tooling and need MLOps.

Can the DevOps team also do MLOps?

The platform team usually owns the tooling, but they need partnership from data scientists and data engineers. MLOps spans skills that DevOps engineers do not always have, such as experiment tracking, feature engineering, and model evaluation. A joint operating model works best.

What is model drift, and why does it matter?

Model drift is the gradual decline in model accuracy because the real world has changed since training. A fraud detection model trained on 2024 transaction patterns can miss new attack types in 2026. MLOps monitoring detects this and either alerts the team or triggers retraining.

Is MLOps relevant for generative AI?

Yes, but the focus shifts. For LLM-based systems, you monitor prompt performance, retrieval quality, hallucination rates, and cost per request rather than training metrics. Many teams use the term LLMOps for this specialised subset.

Which MLOps tools work best in India?

Most Indian enterprises standardise on the managed ML platform of their primary cloud (SageMaker, Vertex AI, Azure ML) and supplement with open source tools like MLflow, Kubeflow, and Feast. The choice usually follows the existing cloud commitment rather than the other way round.