model id + injected seams (+ optional pk field / dated sources)
Optionalassembler: FeatureAssemblyExecutoroptional FeatureAssembly executor override (tests may inject one)
Run-level readiness check (the Record Set Processing Preflight seam): warm-loads
the model + artifact ONCE before the first batch, so a model that needs training
refuses the whole run with one clear message instead of failing every record into
the error-rate circuit breaker. The loaded model is the same warm cache every
record then reuses, so this costs no extra load.
A dry run (context.DryRun) never auto-trains: training creates and publishes a model, repoints the
Record Process and archives the old model, none of which a compute-only run may do. It reports the
ModelNeedsTrainingError instead.
the run-level execution context (user + provider)
Batch-score many records in one sidecar round-trip. The warm model is loaded
once; features for all rows are assembled together (transform-only), then a
single /predict carries every row. Returns one RecordResult per
input record, positionally aligned. Useful for bulk / materialization paths;
the per-record WriteBackProcessor wrapper still owns write-back when the
engine drives records individually.
the records to score (each must carry/resolve a row of data)
the execution context (user + provider)
Score one record. Warm-loads the model on first call, assembles the single
record's features (transform-only), calls the sidecar, and returns the
prediction as the record's ResultPayload. Write-back, if configured, is
applied by the WriteBackProcessor wrapper — not here.
the record to score
the per-record execution context (user + provider)
Record Set Processing scoring processor. Stateless across records except for a warm, lazily-loaded model cache (artifact + frozen contract) shared across a batch. Construct one per Record Process run.