for ML & data teams
Datasets you can actually roll back.
A model is only as reproducible as the data it trained on. Pako versions datasets, weights and artifacts as first-class objects: pin a training run to an exact revision, branch a dataset to experiment, and pull it to every GPU without an egress meter ticking.
the status quo
What this costs you today
Data drift, silently
Someone re-labels a shard, a file is replaced in place, and last week's accuracy number can never be reproduced. The dataset moved and nothing recorded it.
Git LFS falls over at scale
Millions of files and terabytes of tensors are exactly where LFS pointers and per-repo quotas give out.
Egress on every epoch
Pulling a dataset to a fleet of training nodes, repeatedly, is the textbook way to run up a cloud bandwidth bill.
the fix
How Pako fits your pipeline
Content-addressed by design
Identical data is stored once. Branch a 2 TB dataset to relabel a slice and you pay for the slice, not a second copy.
Pin runs to a revision
Every training run references an immutable revision hash. Reproduce a result months later by checking out exactly what it saw.
Pull to the whole fleet, free
Fan a dataset out to a hundred nodes and back, as many epochs as you need. Bandwidth never appears on the bill.
Code and data, together
Keep training code, configs, datasets and weights in one versioned project, so an experiment is one coherent thing you can hand to someone else.
1 hash
reproduce any run, exactly
Pako keeps the heavy work versioned, locked and previewable — with bandwidth always free.
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