This whitepaper explores continuous machine learning (CML) as a method for keeping AI models accurate and adaptive in rapidly changing environments. Page 1 highlights the shortcomings of static ML models, which degrade over time. The CML cycle includes data ingestion, drift detection, automated retraining, validation, deployment, and monitoring. Examples show how real-time signals improve fraud detection, personalization, and threat analytics. The paper emphasizes governance, auditability, and pipeline automation to ensure responsible AI. Organizations gain improved outcomes and faster adaptation to shifting data patterns.

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