Data Lineage and Traceability on the H2O Platform
- Observability
- H2O Driverless AI
- H2O Feature Store
This lesson walks through how every experiment on the platform automatically captures complete lineage, including the exact dataset version, every feature engineering step, and the model configuration behind it, so a prediction can be traced back to its source data. It covers how H2O Feature Store keeps a transformation history for each feature set, letting a team trace any feature back to its source dataset. The lesson also covers automated profiling that flags missing values, anomalies, and potential data leakage before training, and defense in depth access controls from role based permissions to airgapped deployments.
Key takeaways
- How complete experiment lineage lets a prediction be traced back to its exact source dataset and features
- Why H2O Feature Store keeps a full transformation history for tracing any feature to its origin
- What automated profiling catches before training, including missing values and potential data leakage
- How defense in depth access controls combine role based permissions with VPC and airgapped deployments
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