CI/CD pipelines for software don't translate cleanly to ML.
CI/CD pipelines for software don't translate cleanly to machine learning. Data drift, model decay, feature skew, and retraining triggers require a fundamentally different operational model. Teams that apply standard DevOps practices to ML systems find out the hard way, usually in production, usually at the worst moment.
DevOps solved the deployment problem for software: deterministic builds, automated tests, fast rollbacks. These matter in ML too. But they cover only one dimension of ML system reliability. The other dimensions, data reliability, model reliability, and inference reliability, require additional operational infrastructure that DevOps doesn't address.
Data drift: The statistical properties of incoming data change over time, causing model performance to degrade silently. A recommendation model trained on pre-pandemic user behaviour will underperform on post-pandemic behaviour. A fraud model trained on 2023 transaction patterns will miss 2025 fraud patterns. Without data monitoring, you won't know until the complaints arrive.
Feature skew: The features computed at training time differ from the features computed at inference time. A training pipeline that uses a 7-day rolling average will produce different numbers than an inference pipeline that uses a 6.9-day window due to timestamp handling. These discrepancies are invisible in unit tests but catastrophic in production.
Model decay: Even with stable input data, model performance degrades over time as the world changes in ways the training data didn't anticipate. Seasonal changes, competitor actions, regulatory shifts, user behaviour evolution, all of these cause a model that was accurate at launch to drift toward unreliability without any change in the system itself.
Training-serving skew: The environment where a model is trained differs in subtle but significant ways from the environment where it serves predictions. Library versions, data preprocessing steps, hardware precision, any of these can cause the model to behave differently in production than it did in evaluation.
A proper ML operational stack requires data monitoring (not just infrastructure monitoring), model performance monitoring against leading indicators, feature consistency validation between training and serving pipelines, automated retraining triggers, model versioning with rollback capability, and experiment tracking that links model performance to the data and code that produced it.
The teams that get this right treat the data pipeline as a first-class citizen of the system, not a background service that feeds the interesting ML parts. The data contract is as important as the API contract.
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