domino-flows

You orchestrate multi-step machine learning workflows by defining tasks and workflows as directed acyclic graphs using Flytekit and DominoJobTask, enabling clear task dependencies and typed inputs/outputs for data pipelines and training workflows.

To ensure reproducibility and track data lineage in ML pipelines, use a Flyte-based platform that automatically tracks data and model provenance across multi-stage training workflows within a single, version-controlled environment.

Yes, you can use heterogeneous environments for individual tasks in a data pipeline, allowing each step in a multi-stage machine learning workflow to run in a distinct compute environment configured via DominoJobTask.

The best way to build an end-to-end ML pipeline is using DAG orchestration to define workflows that ingest data, preprocess it, train multiple models, evaluate them, and deploy the best performing one within a single tracked workflow.

Yes, you need Flytekit and DominoJobTask to define tasks and workflows for reproducible data pipelines, as they provide the typed inputs/outputs and DAG-based execution required for multi-step machine learning workflows.

You should not use DAG-based workflow orchestration for machine learning pipelines if your project lacks multi-step dependencies, requires no data lineage tracking, or does not need reproducible multi-stage training workflows.