Upcoming feature: Storage for AI, ML and analytics

For Computer Weekly my next feature will look at the specific demands placed on storage architecture by artificial intelligence, machine learning, and analytics.

The piece will ask:

What different approaches to providing storage are there for these technologies? 

What limits, performance considerations and bottlenecks exist with the different approaches?

What ways of providing storage for analytics are we likely to see in future?

The article will cover both on-premises and cloud-based storage, where relevant. I’m keen to include some real-world use cases if possible.

I am open to comment from industry professionals, consultants, analysts and CIOs working with AI. ML and analytics.

Deadline for leads: 1700hrs BST, Tuesday 23 June. As ever, please email in the first instance.

Upcoming commission: storage for machine learning

Data storage is an often-overlooked part of machine learning and other AI deployments.

This article will appear in Computer Weekly. It will cover:

  • Definitions of machine learning/deep learning
  • Its storage requirements including
    • Sizing, capacity, performance (to match compute)
    • Scale
    • Media (SSD vs HDD, hybrids of the two)
    • Parallelism
    • Throughput vs IOPS
    • Locations – including use of the cloud

For this article we are open to comments from vendors, as well as analysts, consultants and other experts. Examples of ML use cases and how systems were designed to run it are most welcome.

Initial pitches and leads by Wednesday May 29th by email please.