What to expect during Pure Accelerate: Join theCUBE June 17

2026-06-10 · Source: AI – SiliconANGLE · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Intermediate, medium

Summary

Everpure Inc., which rebranded from Pure Storage in February, is actively repositioning enterprise storage as an active data platform for the artificial intelligence era. The company has launched several initiatives, including a control plane architecture for ransomware protection and the Everpure Data Stream Beta, providing an automated pipeline from data ingestion to inference. Additionally, Everpure introduced new Portworx capabilities for Red Hat OpenShift users, enabling native Kubernetes storage management across AI workloads, containers, and virtual machines. Its Purity DeepReduce technology targets AI pipelines and backup, and the FlashBlade//EXA system set a SPECstorage Solution benchmark record. These developments will be central to discussions at the Pure Accelerate gathering in Las Vegas on June 17, where theCUBE will provide live coverage. Analysts emphasize that operationalizing data, not model access, is the biggest AI challenge, with 68% of IT leaders citing data accessibility and movement as the primary barrier to scaling AI.

Key takeaway

For AI Architects and MLOps Engineers grappling with scaling AI initiatives, recognize that data operationalization and a unified data foundation are paramount. Your focus should shift from merely managing infrastructure silos to actively integrating storage as a strategic asset. Consider adopting solutions that provide automated data pipelines from ingestion to inference and native Kubernetes storage management to overcome bottlenecks in data accessibility and movement, which 68% of IT leaders identify as the primary barrier to scaling AI.

Key insights

Enterprise storage is transforming into a strategic, unified data platform essential for operationalizing AI at scale.

Principles

• Operationalizing data, infrastructure, and governance is AI's biggest challenge.
• Multi-vendor data strategies are pursued by over 80% of organizations.
• Data accessibility and movement are primary barriers to scaling AI initiatives.

Method

Implement a control plane architecture for cyber resilience and an automated pipeline from data ingestion to inference.

In practice

• Utilize Portworx for native Kubernetes storage management across AI workloads.
• Deploy Purity DeepReduce for AI pipelines and backup workloads.

Topics

• Enterprise Storage
• AI Infrastructure
• Data Management
• Everpure
• Pure Accelerate
• Kubernetes
• Cyber Resilience

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Architect, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI – SiliconANGLE.