Practice 02 ยท Enterprise Data Infrastructure
Data ready when the GPUs are.
Keep every training set, checkpoint and model version at the lowest sensible cost, and stage the ones a job needs onto GPU storage automatically.
01What we deliver
AI and GPU Data Pipelines
- Dataset catalogue. Every dataset indexed with its source, version and licence.
- Staging to GPU storage. Datasets recalled to fast storage before a job starts.
- Checkpoint retention. Checkpoints and model versions kept by policy.
- Cost tiers. Idle data moved to object storage and tape.
- Reproducibility. The exact data behind a model can be found later.
- Multi-site. Data placed where the compute is.
02How it connects
From start to finish.
The path we design around. Every project starts from your own version of it.
01 Collect
Raw data
Sources02 Catalogue
Metadata
Mediaflux03 Keep
Tiers
Object, tape04 Stage
Recall
Policy05 Train
GPU
Clusters05Questions
Questions about ai and gpu data pipelines.
Will recalling data from tape slow down training?
Datasets are staged to performance storage before a job runs, so training reads from fast storage, not tape.
Can we prove which data trained a model?
Yes. The catalogue keeps the dataset version and metadata linked to each model.
06Enterprise Data Infrastructure enquiries
Request a data assessment.
A specialist replies within one working day.
- Sales
- sales@cinthamani.com
- India
- +91 44 2815 3842