DataSieve

DataSieve is a web app that audits and fixes training datasets before you burn GPU time. It scans files in S3/GCS/Azure Blob or local uploads and produces a “trainability report”: label noise estimates, duplication/leakage checks, class imbalance, outliers, PII detection, and drift vs. a reference dataset. It then suggests concrete actions—auto-dedup, stratified re-splits, weak-label cleanup queues, and “do-not-train” filters—plus a reproducible data version snapshot you can hand to your training pipeline. The goal is not to be another end-to-end MLOps platform; it’s a focused pre-training gate that teams can adopt without ripping out existing tooling. You’ll save money by preventing doomed training runs and reduce silent failures caused by leakage and mislabeled samples. It’s an AI app + traditional app combination (heuristics + ML-based detectors).