Get Off Your NAS Webinar #1 - Optimizing IT Infrastructure for AI/ML Workloads

Adopt a modern storage infrastructure to support AI, machine learning, and analytics workloads for the accelerated data center. Listen to a three part Get Off Your NAS webinar series to find out why legacy NAS storage does not support modern workloads.

Adopt a modern storage infrastructure to support AI, machine learning, and analytics workloads for the accelerated data center. Listen to a three part Get Off Your NAS webinar series to find out why legacy NAS storage does not support modern workloads.

Adopt a modern storage infrastructure to support AI, machine learning, and analytics workloads for the accelerated data center. Listen to a three part Get Off Your NAS webinar series to find out why legacy NAS storage does not support modern workloads.

Artificial Intelligence, Machine Learning and High Velocity Analytic workloads are going mainstream. Enterprises of all types and sizes want to seize the opportunity their data presents. As these workloads move from development to production, organizations face a significant challenge with the supporting storage architecture. At the heart of the problem is the file system the organization will use to store the information. It needs to be fast, scalable, durable and cloud-ready.

In this webinar Storage Switzerland and WekaIO will review the various file system options available to organizations looking to create storage architectures for AI, ML and analytics.

In this webinar you’ll learn which file system is best for your modern workload:
•. Traditional NAS (including scale-out NAS)
•. Legacy Parallel File Systems
•. Modern Parallel File Systems

Artificial Intelligence (AI) and Machine Learning (ML) are becoming mainstream initiatives at many organizations. Data is at the heart of AI and ML. Immediate access to large data sets is pivotal to successful ML outcomes. Without data, there is no learning. The goal of AI and ML is to try to simulate human thinking and understanding. AI and ML initiatives cannot however be realized unless the data processing layer has immediate access to, and a constant supply of, data.