MLOps & Deployment

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MLOps is the set of practices and tools for reliably developing, deploying, and maintaining machine-learning systems. It extends software delivery with data and model concerns such as reproducible training, validation, model and dataset versioning, monitoring, and retraining decisions. MLOps matters because model behavior depends on data and can change over time, so deployment is the start of ongoing operations, not the end.

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Model drift is a decline in predictive usefulness as the relationship between production data and the task changes. Data drift describes a change in input distributions; concept drift describes a change in how inputs relate to outcomes. Teams detect possible drift by monitoring inputs and delayed performance, investigate the cause, and validate a response such as updating data or retraining before release.