Reduce Regression Testing from 200 Hours to 40 Without Adding More Work
2025.12.09-PureStorage-LandingPage-1540x660

Sponsored By:

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Tuesday, December 9

3 pm ET

The explosion of agentic AI is creating unprecedented opportunity, but it has also exposed a critical flaw: today’s data centers are no longer fit for purpose. AI requires data pipelines engineered for high performance, massive scale, and real-time concurrency—requirements that strain the limits of conventional architectures. The secret to AI success? Getting your data right.

Join us to explore the latest thinking in AI infrastructure—from data pipelines to AI factories. This session examines the architectural shift needed to move AI forward: from complex, brittle data pipelines to a unified, frictionless data foundation. Learn how to deliver high-quality, high-performance data to your AI and ML teams—affordably and at scale. This isn’t about incremental improvement; it’s about reimagining how infrastructure is built for AI, enabling teams to focus on innovation rather than complexity.

Key Takeaways:

1. Accelerate AI Performance. Deliver high-quality, high-performance data pipelines to fuel model training and inference.

2. Simplify the AI Data Path. Eliminate complexity by unifying fragmented storage systems into a single, high-performance data foundation.

3. Build the Foundation for Your AI Factory. Transform your data center into a nimble, software-defined platform that adapts seamlessly to evolving AI demands.

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Erin Headshot-modified

Erin Stevens

AI & Data Analytics Leader - Pure Storage
Erin Stevens is an AI Infrastructure and Data Strategy Specialist at Pure Storage. Before joining Pure, she helped build the data analytics team at Tableau and worked closely with analysts to design better-performing data pipelines. Today, she brings that hands-on experience to the world of AI infrastructure—helping enterprises rethink how their data foundations are built for speed, scale, and innovation.