How do you architect ML systems to accommodate model evolution over time, including architecture changes, retraining, and A/B testing, while maintaining stability and minimizing risk?

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Designing for model evolution requires treating ML systems as continuously changing rather than static deployments. The foundation is a robust versioning strategy. Every model artifact needs a unique version identifier linking it to its training data, code, hyperparameters, and…