Pipelines Overview

Dataloop’s Pipeline

Create automated models that weave together humans and machines to process data in a pipeline architecture – a series of nodes, where each node’s output is the input of the next one.

The Dataloop pipeline process allows transitioning data between

• labeling tasks
• quality assurance tasks
• functions installed in the Dataloop system
• code snippets
• machine learning (ML) models

Your data can be filtered by any criteria, split, merged, and change status in the process.

Altogether, Dataloop’s pipeline can
🗸  facilitate any production pipeline
🗸  preprocess data and label it
🗸  automate operations using applications and models
🗸  postprocess data and train models of any type or scale at the highest performance and availability standards

The following example shows a pipeline where data is preprocessed by code (e.g., a video is cut into frames) and then directed to three different tasks that run in parallel. The items marked as completed are sent to a separate task (e.g., QA task), whereas the items that are of status discard are sent to a separate dataset.