What is ML Model Deployment?
Turkish: Model Deployment
Model deployment turns a trained machine learning model into an API, batch job, or edge service that runs on production data.
What is Model Deployment?
Model deployment turns a trained model from an experiment into a production component that responds to real data. The model may run as an API, a scheduled batch job, part of a data pipeline, or on an edge device.
Process Steps
The model file, dependencies, feature calculation code, and configuration are packaged first. Then a serving endpoint, batch schedule, or streaming consumer is created. Before release, teams test input schema, prediction latency, memory use, error behavior, and security boundaries.
Production Concerns
Publishing the model is not the end of the work. Data distributions can shift, model drift can appear, prediction cost can rise, or edge cases can create unexpected decisions. Shadow deployment, canary release, rollback plans, model versioning, and monitoring are part of a safe release process.
Machine learning explains how the model is trained. CI/CD practices help ship the model package in a repeatable and controlled way.
Related Terms
CI/CD makes software releases repeatable by moving code changes through automated build, test, and deployment pipelines.
Machine LearningMachine learning trains models on data patterns so software can make predictions, classifications, or decisions on new examples.
MLOpsMLOps manages machine learning data, training, deployment, monitoring, and retraining with production engineering discipline.