AI Field Day 6: ML Commons and Measuring the Right Things

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Jay Cuthrell

Chief Product Officer

Hi, I’m Jay Cuthrell and I’m a delegate for Tech Field Day this week for AI Field Day 6 in Silicon Valley on January 29–January 30, 2025. I’m sharing my live blogging notes here and will revisit with additional edits, so bookmark this blog post and subscribe to the NexusTek newsletter for future insights.

Better. Faster. Cheaper?

When a skeptic encounters a claim, the examination begins. The biggest story of the past few weeks (months?) has been the impact of a new AI model that challenges prior assumptions.

So, how does the skeptic make a valid comparison. Next, how does this apply to the seemingly non-stop claims of better, faster, and cheaper ways to bring AI into everything everywhere imaginable.

There is also a saying that “if you have to ask they can tell you anything”. As vendors are constantly innovating and bringing those innovations to market, the benchmark for better or improved is arguably best kept open and transparent.

ML Commons

ML Commons

MLPerf and Moore's Law

MLPerf and Moore's Law

The Goals of ML Commons

During AI Field Day 6, David Kanter of ML Commons shared perspectives on the goals of MLPerf for getting more results into GitHub for transparency and traceability. To Wardly this out a bit, the goal is to be associated with the leading direction and sensibly retiring benchmarks that are no longer represented as performance, efficiency, and qualitative (risk, responsibility) — more pioneer within the edge of a settler than attention to the laggard claims of best.

Learn more… https://github.com/mlcommons

MLPerf Storage Divisions and Categories

MLPerf Storage Divisions and Categories

MLPerf Storage submitters

MLPerf Storage Submitters

Architectures and Innovations

Also during AI Fields Day 6, Curtis Anderson explained the challenges within storage systems that are slow to change (architecturally) with the rapid innovation timelines of AI. As such, past storage specific benchmarks for storage are not suitable for the nature of the AI workloads that use storage in vastly different ways than a traditional Enterprise workload — and contrary to what you might have heard — storage is not “dead”.

Learn more… 

https://github.com/mlcommons/storage 

Industry Pace and Rate of Claims

As a side note, more and more claims will be made as models come to market. It will be interesting to see how benchmarks evolve in response.

ML Commons Techmeme

ML Commons Techmeme

About the Author

Picture of Jay Cuthrell

Jay Cuthrell

Chief Product Officer, NexusTek

Jay Cuthrell is a seasoned technology executive with extensive experience in driving innovation in IT, hybrid cloud, and multicloud solutions. As Chief Product Officer at NexusTek, he leads efforts in product strategy and marketing, building on a career that includes key leadership roles at IBM, Dell Technologies, and Faction, where he advanced AI/ML, platform engineering, and enterprise data services.

Let NexusTek drive your business toward a more innovative and secure future.

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