<ul data-eligibleForWebStory="true"><li>Cross-validation is integral for robust, observable ML systems from training to deployment.</li><li>Modern ML Ops integrates cross-validation into CI/CD, A/B testing, and monitoring frameworks.</li><li>Use cases span diverse sectors like e-commerce, fintech, health tech, automotive, and social media.</li><li>Strategies include Python orchestration, Kubernetes deployment, and experiment tracking for reproducibility.</li><li>Failure modes, risk management, system optimization, monitoring, security, and best practices discussed.</li></ul>

Machine Learning Fundamentals: cross validation tutorial

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