What Is MLOps? The Discipline That Extends DevOps to Machine Learning

MLOps, or Machine Learning Operations, extends DevOps principles to machine learning projects, aiming to bring automation, reliability, and repeatability to data, models, and pipelines. It addresses the unique lifecycle challenges of ML systems, which involve code, data, and trained models as core artifacts requiring versioning and management.

Helena Strauss

March 31, 2026 · Updated October 27, 2026 · 4 min read

Machine Learning Operations, or MLOps, is a set of engineering practices specific to machine learning projects that borrows from the more widely adopted DevOps principles of software engineering. Its purpose is to bring the same automation, reliability, and repeatability that DevOps brought to application code to a far messier set of artifacts: data, models, and the pipelines that connect them. If you already understand DevOps, you are most of the way to understanding MLOps — but the differences matter, because machine learning introduces lifecycle problems that code alone never had.

Amazon Web Services frames the two practices as siblings: both aim to improve the processes where you develop, deploy, and monitor software applications, and DevOps exists to bridge the gap between development and operations teams. MLOps applies that same bridging logic to systems whose behavior depends on data and trained models rather than on source code alone.

MLOps vs. DevOps: A Comparative Overview

The clearest way to understand MLOps is to place it side by side with DevOps across the dimensions that define how a system is built, tested, and kept healthy in production. The comparison below isolates exactly where machine learning adds complexity to an otherwise familiar engineering discipline.

MLOps compared with DevOps across four operational dimensions
Dimension	DevOps	MLOps
Core Focus	Automating the software development lifecycle for traditional applications, bridging development and operations teams.	Extends DevOps principles to the unique challenges of ML model development, deployment, and maintenance.
Key Artifacts	Code is the primary artifact for versioning and management.	Code, data, and trained ML models — three core elements of ML-based software, per ml-ops.org — all require versioning and management.
Testing & Validation	Continuous integration and delivery focused on code quality and functionality.	Continuous integration extended to validate and test data and models alongside code, including experiment tracking and model training validation.
Monitoring	Application performance and system health — uptime and resource utilization are the typical concerns.	Model performance plus data drift and concept drift, monitored in production so corrective action can be taken when training and inference data diverge.

Read across the rows and a pattern emerges: MLOps does not discard DevOps, it layers new obligations on top of it. DevOps treats code as the artifact that changes and the thing you test. In a machine learning system, code is only one of three moving parts. The data feeding the pipeline shifts over time, and the model trained on that data can silently degrade even when the application code is untouched.

That last point is the crux. A traditional application behaves the same way on the day it ships as it does a year later, assuming the code and infrastructure hold. A deployed model does not. Databricks describes the operational answer: automated model retraining, with alerts and automation that take corrective action in case of model drift caused by differences between training and inference data. This is a category of failure — and a category of response — that classic DevOps monitoring was never designed to catch.

Core Components of an MLOps Workflow

So what does an MLOps system actually consist of? The community reference site ml-ops.org breaks ML-based software into three core elements — data, ML models, and code — and describes the machinery that handles each: data engineering pipelines, ML pipelines, and the ML workflows that tie them together. Those components, plus automation borrowed from DevOps, form the practical skeleton of any MLOps setup.

Data engineering pipelines. The ingestion and preparation machinery that moves raw data into a form suitable for training. Because model quality depends on data quality, the pipeline itself becomes something to validate and monitor, not just a means of transport.

ML pipelines. The training machinery that turns prepared data into a registered, versioned model. Databricks describes this pre-production stage as automated with CI/CD tools such as repositories and orchestrators — DevOps tooling repurposed for a new artifact.

Model deployment and monitoring. The trained model is served as a prediction service for applications. AWS describes this final stage as one where you collect statistics on the deployed prediction service from live data, and those statistics become the trigger to run the pipeline again or start a new experiment cycle.

Automated retraining. The feedback loop that closes the system: when monitoring detects drift between training and inference data, alerts and automation trigger retraining rather than waiting for an engineer to notice degraded predictions.

Notice the shape of this workflow: it is a loop, not a line. AWS's pipeline description ends where it begins — deployed-model statistics feeding the next experiment cycle — and Databricks's retraining automation makes that loop self-correcting. This cyclical quality is what distinguishes an MLOps workflow from a one-off model build, and it is why the practice borrows so heavily from CI/CD thinking: the only way to keep a loop turning reliably is to automate its stages.

One caveat is worth stating plainly. MLOps is a set of practices and structural concepts, not a specific product. This article explains the principles; it is not a tutorial for implementing any particular MLOps stack, and the technical details of individual machine learning algorithms sit outside its scope. The components above describe what any serious MLOps approach must account for, whichever tools you ultimately choose.

Embracing MLOps for Robust ML Systems

The decision in front of any team shipping machine learning is whether to adopt MLOps practices to ensure the reliable, efficient, and continuous deployment and maintenance of machine learning models in production environments. The alternative — manual handoffs between data scientists and operations, unversioned datasets, and models that decay unnoticed — is precisely the gap that both DevOps and MLOps were created to close, extended to the data-and-model artifacts that ML adds.

How do you know adoption is working? The measurable indicators are a streamlined ML lifecycle, reduced time-to-market for new models, and improved model performance and stability in production. Those outcomes are observable: watch how long it takes a retrained model to move from experiment to a monitored production endpoint, and whether drift alerts trigger automated retraining instead of silent degradation. When the loop turns on its own, MLOps is doing its job.

What Is MLOps? The Discipline That Extends DevOps to Machine Learning

MLOps vs. DevOps: A Comparative Overview

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Core Components of an MLOps Workflow

Embracing MLOps for Robust ML Systems

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