Deploy
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
Deployment moves a trained machine learning model from development into production, where it serves predictions that real users and systems act on.
What is Deploy?
To deploy a machine learning model is to move it from development into production, where it receives live inputs and returns predictions that people or systems act on. Deployment is the point where a model stops being an experiment: before it, a bad prediction wastes an afternoon in a notebook; after it, a bad prediction reaches a customer.
Types of Deployment
How you deploy depends on how fast the answer is needed and where the data lives. Three patterns cover most production systems.
- Batch deployment scores large sets of records on a schedule, like a churn model that ranks every customer overnight. Nobody waits on an individual prediction, so throughput matters more than latency.
- Real-time API deployment puts the model behind an endpoint that answers one request at a time while the caller waits. A fraud check that runs as a card payment clears works this way.
- Edge deployment installs the model on the device itself: a phone, a camera, a car. Inference runs locally, with no network round trip and no data leaving the device.
Deployment is not the finish line either. Production data shifts away from the training data over time, so deployed models need monitoring for data drift and a plan for retraining.
Example of Deploy
A bank builds a fraud detection model. After model training and evaluation, the team registers the winning version in a model registry and deploys it as a real-time API. The payment system calls the endpoint on every transaction and blocks the ones scored as likely fraud.
Two months later, monitoring shows the false-positive rate creeping upward: fraud patterns have shifted. The team retrains on recent data, evaluates the new version against the old one, and redeploys – the loop that deployment starts rather than ends.
Related AI terms: Pipeline · Model Registry · MLflow · Inference · Data Drift
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About