Definition
Immutable artifacts are content-addressable blobs identified by cryptographic hashes. Lineage graphs track how artifacts transform through pipelines—enabling reproducibility and impact analysis.
CONTENT-ADDRESSABLE STORAGE
Every model, dataset, and feature schema is stored with SHA-256 hash as ID. A signed manifest lists all dependencies with hashes. Any change produces a different hash, making drift immediately visible. Training produces a manifest: model binary hash, dataset hash, code commit, library versions.
LINEAGE GRAPHS
Nodes represent versioned artifacts (raw data, features, models). Edges represent transformations with config fingerprints. When a data source requires deletion (GDPR), lineage identifies all downstream artifacts that depend on it—triggering retraining or impact assessment.
💡 Insight: If a bug is found in a feature computation, lineage identifies which models need retraining and which predictions may be invalid—critical for incident response.
ACHIEVING REPRODUCIBILITY
Pin library versions with lock files. Use container digests, not mutable tags. Fix random seeds. Record hardware fingerprints. Snapshot training data as immutable manifests rather than live queries. Where nondeterminism is unavoidable, define acceptable tolerance and validate with calibration datasets.
⚠️ Trade-off: Full immutability increases storage costs. Use tiered storage—hot for recent artifacts, cold archive for older versions retained for compliance.
✓Content addressable storage using Secure Hash Algorithm 256 (SHA 256) hashes makes every artifact (model, dataset, feature schema) immutable and tamper evident, any change produces a different hash visible in manifests
✓Signed manifests list all dependencies with cryptographic hashes and are themselves signed, ensuring review boards and deployment systems reference the exact artifacts that were approved, preventing bait and switch attacks
✓Data lineage graphs connect raw sources through transformations to models, enabling impact analysis where a General Data Protection Regulation (GDPR) deletion or bug discovery automatically identifies all affected downstream models requiring retraining
✓Reproducibility demands pinning library versions (pip freeze), using container digests not mutable tags (latest is forbidden), fixing random seeds, and snapshotting training data as immutable manifests rather than live queries that drift over time
✓When nondeterminism is unavoidable (distributed training, floating point variance across Graphics Processing Unit or GPU types), define acceptable tolerance (Area Under the Curve or AUC differs by less than 0.01) and validate with calibration datasets
✓Meta and Google use lineage for incident response where a feature bug running 90 days triggers automatic identification of affected models and potentially invalid predictions requiring notification or recomputation
1Training manifest: {"model": "sha256:a1b2c3", "dataset": "sha256:d4e5f6", "features": "sha256:g7h8i9", "code": "git:123abc", "container": "docker@sha256:xyz789", "random_seed": 42, "libs": "sha256:requirements_lock"} signed with private key, any tampering breaks signature verification
2Lineage query: MATCH (d:DataSource {id:'transactions_q1'}) to (m:Model) RETURN path shows transactions_q1 → fraud_signal_v2.3 → features_v47 → training_j1829 → model_m92, enabling targeted retraining when source is affected by deletion or corruption
3Reproducibility test: Rerun training job with same manifest on different hardware (Tesla V100 vs A100 GPUs), verify model outputs differ by less than 0.5% on 10K held out examples, accept as equivalent despite floating point variance