What is an 'asset' in Dagster and why is it useful for ML?

hard

Answer

$Asset = data artifact (dataset, model, feature) with computation defined declaratively.$
$Dagster tracks: which upstream assets it depends on, when it was last computed, whether it's stale.$
$Enables: (1) materialization on demand ('compute only what's needed').$
$(2) natural ML thinking (features + labels + models as assets).$
$(3) automatic lineage graph.$
$(4) partitioned assets for date-based data.$
$Vs Airflow's task view: assets are the deliverables, tasks the how.$

Check yourself — multiple choice

Random
Data artifact with declarative computation + tracked upstream deps + staleness; enables materialization on demand + ML-natural thinking + auto-lineage + partitions
Same as task
Not real

Dagster asset: declarative + deps + staleness + materialize on demand.

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