Feature mapping for data scientists who know their data before they model it.
Feature mapping sessions diagram which signals go into a model, where they come from, and how they're transformed. Drawing this on a whiteboard with the full team — data engineers, ML engineers, product — surfaces dependencies and gaps before they become blockers.
Why data scientists love this workflow
Feature mapping is the bridge between raw data and a model that works. Which signals are available at inference time? Which require joins across multiple tables? Which need aggregations that add latency? These questions have to be answered before training, not discovered at deployment.
BoardSnap reads your feature map — data sources, transformation pipelines, feature names, and availability flags — and produces a structured feature specification. The spec documents what goes into the model, where it comes from, and what needs to be built before training can start.
The exact flow
- List all candidate features
Write every feature being considered — raw and derived. Don't filter yet. Get everything visible.
- Map each feature to its data source
Draw arrows from each feature back to its source table, event stream, or API. Show the join or aggregation required.
- Flag availability at inference time
Mark each feature as available at training only, available at inference, or requiring a real-time lookup. This is the critical filter.
- Identify missing features and data engineering work
Features that need new pipelines or tables become action items for the data engineering team.
- Snap the feature map
Open BoardSnap and capture. The full feature specification is documented in one shot.
What you'll get out of it
- Feature availability at inference time is documented before training begins
- Data engineering work is identified early — not discovered at deployment
- Feature sources and transformation logic are captured in the spec
- The feature map is shareable with data engineering and product teams
- Feature maps from different model versions are searchable for comparison
Frequently asked
Can BoardSnap read feature maps with data source arrows and transformation labels?
Yes. Arrows from features back to data sources are read as relationships. Transformation labels — 'rolling 7-day avg,' '30-day window,' 'log transform' — are captured alongside the feature name.
How does this help prevent training-serving skew?
Documenting exactly which features are available at inference time — and flagging any that require real-time lookups — is the first step in preventing skew. The feature map is your contract between training and serving.
Can I share the feature map with the data engineering team?
Yes. The BoardSnap summary describes each feature, its source, and any required transformations in plain language. Data engineers can read it without ML background and build the pipelines accordingly.
What if some features are still experimental?
Mark experimental features clearly on the board — 'TBD,' 'depends on data availability,' 'needs validation.' BoardSnap captures these flags in the output so they're visible in the spec.
Data Scientists: try this on your next feature mapping.
Three taps. Action items in your hand before the room clears.