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Software Engineering vocabulary · Machine Learning and Data Processing

MLOps

Meaning in software engineering, with examples

/ˌem el ˈɑːps/nounCommon term

In software engineering, MLOps means: The practices and tools for running machine learning reliably in production: versioning data and models, automating training and deployment, and monitoring models over time.

In Turkish: MLOps, makine öğrenmesi operasyonları

How it is used

Built on DevOps ideas. Used for a team (the MLOps team), a role (MLOps engineer) and a set of tools: experiment tracking, model registry, feature store, CI/CD for models and monitoring. For LLM systems some people say LLMOps.

Example sentences

  1. Without proper MLOps, nobody knew which model version was in production.

  2. Our MLOps pipeline retrains the model every Monday and deploys it if the tests pass.

Common collocations

  • MLOps engineer
  • MLOps pipeline
  • MLOps platform
  • model registry
  • deployTo put a new version of software onto servers or devices so that it starts running there, for example in a test environment or for real users.
  • data driftA change over time in the data a deployed model receives, compared with the data it was trained on, which can slowly make its predictions worse.
  • checkpointA point in a level where the game saves the player's progress, so after failing or dying they start again from there instead of from the beginning.
  • endpointA specific URL of an API that accepts requests for one kind of action, such as GET /users to list users or POST /orders to create an order.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Machine Learning and Data Processing

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