Data Observability Meets AI: Ensuring Trustworthy Intelligence at Scale

Rajesh Sura, Head of Data Engineering and Analytics at Amazon, presents "Data Observability Meets AI: Ensuring Trustworthy Intelligence at Scale." Timestamps: 00:00 Introduction: Trust as the foundation of AI 01:22 Risks in data pipelines and consequences of poor data quality 02:53 Data observability pillars: monitoring, proactive detection, validation 04:41 Decision-making pyramid: Data → ML → Business impact 10:17 Five pillars of observability: freshness, volume, schema, lineage, quality 12:43 AI-augmented observability: predictive & autonomous correction 19:00 Common pitfalls and best practices for implementation 24:00 Responsible AI: fairness, transparency, compliance & end-to-end trust In the era of AI-powered decision-making, trust in data is no longer optional - it’s foundational. As organizations scale their analytics and machine learning initiatives, the integrity of the underlying data pipelines becomes a critical risk factor. Enter Data Observability—the practice of continuously monitoring, tracking, and validating data systems to detect issues before they become business-impacting. But observability itself is evolving. This session explores the convergence of AI and data observability, highlighting how intelligent agents, anomaly detection models, and predictive diagnostics are being embedded into modern data stacks. It delves into architectural patterns for AI-powered observability, showcases real-world use cases where automated monitoring prevented data downtime, and discusses how this evolution enables self-healing data pipelines, improves model reliability, and boosts executive confidence in AI-driven insights. Official session recording from the Applied AI Summit 2025 Connect with us: Our website: https://www.johnsnowlabs.com/ LinkedIn:   / johnsnowlabs   Facebook:   / johnsnowlabsinc   X: https://x.com/JohnSnowLabs #DataObservability #DataDrivenAI #DataQuality #DataIntegrity #DataMonitoring #AITrust #MLObservability #ResponsibleAI