A significant portion of the architectural discussion revolves around the . In many organizations, features (inputs used by models) are calculated multiple times by different teams, leading to "training-serving skew"—where the data used to train the model differs slightly from the data used in production.
: Building efficient CI/CD (Continuous Integration/Continuous Deployment) workflows specifically for ML models. Model Management
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In traditional software, CI/CD deals with code. In MLOps, it extends to data and models. Jhajj outlines how to automate the testing of ML code, data schemas, and model performance. Continuous Delivery ensures that the model can be deployed to production automatically once it passes predefined benchmarks. Data and Model Versioning