Dataset Fingerprinting and Artifact Versioning Strategies

Dataset fingerprinting is the practice of creating a cryptographic signature or version identifier for training data that allows you to prove two runs used exactly the same inputs. Without this, you might think you reproduced an experiment when in fact the underlying data changed silently due to schema evolution, backfill corrections, or deletion policies. Content addressing means computing strong hashes like SHA256 for datasets and using that hash as the artifact identifier. For a 100 GB dataset, you might compute per-shard hashes and store a manifest, enabling partial verification without reading everything. The critical trade-off is exact data snapshots versus time travel reads. Copying entire datasets ensures immutability but becomes prohibitively expensive at 10 TB or larger scales. A full copy of a 5 TB dataset costs significant storage and takes hours to materialize. Time travel in data lakes and feature stores plus content hashing is cheaper, relying on underlying storage guarantees like Delta Lake or Iceberg to read data as it existed at a specific timestamp. This requires careful retention policies; if source systems delete data within your lookback window, reproducibility breaks. Uber Zipline versions feature definitions and materializations so training and serving read consistent time traveled snapshots. A marketplace model might reference a specific backfill version covering a 90 day window with explicit watermark boundaries. Google TFX relies on immutable artifact fingerprints and time traveled reads from internal storage to guarantee that ExampleGen reads identical data when a pipeline is rerun. The pattern is to log source tables, snapshot or version IDs, query predicates, and the explicit list of sample IDs per split for perfect reproducibility. Artifact retention quickly becomes a cost problem. Storing every checkpoint for every run leads to multi-TB growth monthly. A typical solution uses lifecycle policies: keep all artifacts for 30 days, then keep only top k performers per experiment, and deduplicate identical artifacts via content hashing. For a mid size org generating 85 GB per day, deduplication and top k retention can reduce storage by 3x to 10x. Netflix Metaflow uses content addressed paths in object storage, naturally deduplicating identical model weights or evaluation reports across runs.

Uber marketplace model references Zipline backfill version covering 90 day window with watermark boundaries, ensuring training and serving feature consistency across 100 million labeled rows

Netflix Metaflow content addressed paths: /artifacts/<SHA256_hash>/model.pkl naturally deduplicates identical model weights from repeated hyperparameter search runs

Dataset manifest example: {"dataset_id": "user_events_v2", "snapshot_timestamp": "2024-01-15T00:00:00Z", "total_rows": 1500000000, "shard_hashes": ["a3f2...", "b7e1..."], "split_sample_ids": {"train": [123, 456], "val": [789]}}