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Learn how effective data management serves as the critical foundation for AI system success in this conference talk that reveals why artificial intelligence systems fail in production due to data management issues rather than algorithmic limitations. Discover practical approaches for building resilient data foundations using cloud native technologies, including implementation patterns with OpenTelemetry for comprehensive data pipeline observability, Kubernetes and Kubeflow for management and orchestration, and Prometheus for monitoring data quality metrics at scale. Explore critical governance frameworks necessary for AI-ready data platforms and examine how organizations can establish sustainable data management practices that support both traditional workloads and AI applications. Understand why focusing solely on model selection and hyperparameter tuning is insufficient without addressing the underlying data infrastructure challenges that determine whether AI systems succeed or fail in real-world deployments.
