Downstream task
A downstream task is a later application or evaluation task that uses the output, model, or learned representation produced by an earlier task or training process.
Data Pipelines with Apache Airflow, Second Edition
A downstream task is a workflow task that follows another task in the dependency structure and may depend on its completion.
- How it is used
- Uses downstream relationships to route, condition, join, or suppress Airflow workflow tasks.
- How deep it goes
- Explains dependencies, branch selection, skip propagation, and trigger rules through working Airflow task arrangements.
- Examples
- Uses old and new ERP fetch-clean branches, join_datasets, and latest-only model deployment.
- What you can do after
- Apply branching and conditional execution to control which Airflow tasks run, skip, or continue.
Read how Data Pipelines with Apache Airflow, Second Edition explains downstream task →
Deep Learning with R, Third Edition
A downstream task is the target application for which a pretrained model or representation is subsequently adapted or evaluated.
- How it is used
- Positions a final application as the target for adapting or evaluating a pretrained model or representation.
- How deep it goes
- Defines task transfer and specialization but stops short of detailing adaptation procedures, architectures, losses, or evaluation methods.
- Examples
- Uses text classification as the concrete example following sequence-model pretraining.
- What you can do after
- Apply it via fine-tuning a pretrained model for a specific final application.
Read how Deep Learning with R, Third Edition explains downstream task →
Sutskever's List
A task evaluated after model training to measure practical performance on a particular capability or application.
- How it is used
- Evaluates practical capability after training, complementing forecasts based on training loss.
- How deep it goes
- Contrasts predictable training-loss scaling with uneven downstream performance and notes that benchmarks offer only a partial view.
- Examples
- Uses arithmetic, coding, planning, mismatched flag-and-cake generation, and right-versus-left shoe tying.
- What you can do after
- Evaluate model capability with general benchmarks while recognizing that loss forecasts do not guarantee task performance.
Related concepts
- foundational modelused by · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
- fan-out/fan-in patternused by · The fan-out/fan-in pattern distributes work or data across multiple concurrent paths and then combines their results.
- trigger ruleconfigured by · A trigger rule is a condition determining whether a workflow task may execute based on the execution results of its dependencies.
- generatoraddressed by · A generator is a computational component or construct that produces data or values from inputs, representations, context, or specified constraints.
- workeruses · A worker is a person or computational execution unit assigned to perform a task as part of a larger system or activity.
- fine-tuninguses · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
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