ML best practices, product announcements, and engineering deep dives from the GradientPond team.
Our biggest release yet brings support for 10,000+ GPU clusters, automatic sharding, and 40% faster checkpoint recovery. Here's what's new and how to upgrade.
Read more →A deep dive into the engineering behind our new checkpoint system that saves 70B+ parameter models in under 30 seconds using memory-mapped files and async I/O.
Read more →From naming conventions to metric hierarchies, these patterns will help your team maintain reproducibility and accelerate iteration cycles across projects.
Read more →PBT is now available in GradientPond. Automatically tune hyperparameters during training with evolutionary strategies that adapt in real-time.
Read more →When you're ingesting 2 million metric points per second, off-the-shelf databases don't cut it. Here's how we built a custom storage engine for ML telemetry.
Read more →How to structure your model registry for production ML: staging environments, promotion gates, rollback strategies, and automated validation pipelines.
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