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Artificial Intelligence & Machine Learning vocabulary · Deployment and MLOps

Deploy

Meaning in artificial intelligence & machine learning, with examples

/dɪˈplɔɪ/verbCore term

In artificial intelligence & machine learning, deploy means: To put a trained model into a real system so that users or other software can send it requests and receive predictions.

In Turkish: canlıya almak, devreye almak, yayına almak

How it is used

You deploy a model to production, to a server, to the cloud or to a device. The noun is deployment. Teams talk about deploying a new version, rolling it out gradually and rolling it back if something breaks.

Example sentences

  1. We deployed the new ranking model on Tuesday, and click-through went up by 3%.

  2. Can we deploy the model on the phone itself, or is it too big?

Common collocations

  • deploy a model
  • deploy to production
  • model deployment
  • on-device deployment
  • inferenceThe stage in which a trained model is used to produce outputs for new inputs, for example answering a user's question or classifying a new photo.
  • endpointA URL where a deployed model can be reached over a network: other software sends a request to it and receives the model's prediction or answer.
  • MLOpsThe practices and tools for running machine learning reliably in production: versioning data and models, automating training and deployment, and monitoring models over time.
  • roll outTo make a new model, feature or version available to users, often in stages, starting with a small share of traffic and growing as long as the metrics look good.
  • latencyThe time between sending a request to a model and receiving its response, usually measured in milliseconds or seconds.

This word in other fields

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

More terms: Deployment and MLOps

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