The enterprise DevOps landscape is undergoing a fundamental shift from cloud-native to AI-native operations. Organizations now face the complex challenge of seamlessly integrating traditional infrastructure with cutting-edge AI development workflows, requiring purpose-built data infrastructure that bridges legacy systems and modern AI pipelines at enterprise scale.

NetApp exemplifies this evolution. Over the course of eight years, their engineering organization transformed Site Reliability Engineering from reactive monitoring to proactive, AI-native DevOps operations using InfluxDB's time-series database. Their implementation demonstrates how strategic data infrastructure enables unified operational management across diverse workloads, from legacy systems to AI inference engines.

In this exclusive case study, we'll examine NetApp's comprehensive approach to AI-native DevOps and reveal practical insights from their 8-year optimization journey. Learn actionable strategies for building resilient, intelligent operations that prevent system failures while enabling AI-driven innovation at enterprise scale.

In this session, you’ll learn:
• How to build a data infrastructure that bridges legacy systems with modern AI pipelines in enterprise environments
• About NetApp's 8-year journey transforming from reactive monitoring to proactive AI-native DevOps operations
Practical strategies for unified operational management across diverse workloads using time series databases