Operations | Monitoring | ITSM | DevOps | Cloud

Training is at the heart of every LLM model, but it’s still an application running on an infrastructure, which means it can fail. Our GPU test helps you test your training GPUs so you don’t lose that valuable work. TRANSCRIPT: One of the things we built recently was the GPU Gremlin. So if you are training a bunch of models and you're doing a bunch of GPU testing. You know, we want to give you the tools to be able to go test that, to understand how training the model could fail.

Reliability testing isn’t a one-and-done thing. You need to test on a regular schedule to make sure your system is reliable in the face of changing systems.

Do you know how your system will respond when major outages strike? Disaster Recovery Testing safely simulates real catastrophic failures across your entire system. You can centrally and easily run zone, region, and datacenter-scale reliability tests across your entire organization simultaneously for disaster recovery, business continuity, compliance verification, and more. With Disaster Recovery Testing, tests that used to take engineering-months and dozens of experts can be done safely and securely in hours by a single person.

In this clip from an AI roundtable with Gremlin, Nobl9, and PagerDuty, Mandi Walls talks about how companies will want to audit AI to keep it reliable.

AI operates on the same systems and infrastructure as every application, which means if you want to keep it reliable, you have to keep the systems underneath it reliable. Gremlin CEO Kolton Andrus explains more in this clip from an AI reliability roundtable with @nobl9inc and @Pagerduty.

In this webinar clip, Alex Nauda, CTO of Nobl9, explains how keeping AI reliable means changing how you look at SLOs.