Overview

Machine Learning (ML) workflows, like the AI Studio ML models, consist of multiple steps from libNLP. The steps within a workflow typically include:

• data loading

• data cleaning

• pre-processing

• classification

• generation of entities

In a pipeline with similar ML workflows, it is likely that identical steps are contained in multiple workflows. For example, the identical sentence splitting or PDF sentence tokenization step can appear in all or multiple ML workflows present in the pipeline.

To avoid multiple executions of identical steps and increase the performance of the processing pipeline, you can enable the optimization of ML workflow execution.

Configuration

In the Server space under Configuration set the machinelearning.optimize.workflows to true:

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This page can now be found at ML Enrichments for Pipeline Workflows on the Squirro Docs site.