MLOps vs DevOps: A Practical Guide for Data Scientists and IT Teams
Summary
MLOps extends DevOps by governing code, datasets, and model artifacts, adding Continuous Training pipelines to automatically retrain models when data drift exceeds thresholds. This guide details a three-layer model for successful MLOps, leveraging DevOps CI/CD, ML orchestrators, and unified monitoring to close the feedback loop.
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