product updates, company news, and insights on building and optimizing your data pipelines.
You don't notice egress until you see the bill. Here's how ML training pipelines quietly rack up cross-region transfer costs, and the architecture that fixes it.
Writing a 500GB checkpoint every hour stresses your storage in ways that training data doesn't. Here's how to design a checkpoint pipeline that's fast, reliable, and doesn't cost a fortune.
Synthetic storage benchmarks lie about what DataLoader performance feels like in practice. Here's how to measure what your training pipeline actually cares about.
POSIX semantics on top of object storage is an old and messy problem. Here's what's possible, what's impossible, and what ML teams should actually demand from a storage layer.
A caching gateway colocated with your GPUs is the biggest single lever for training throughput. Here's how the architecture works and why it produces such dramatic speedups.