Machine learning pipeline with visualizations
AssigneeFujitsu Limited
InventorsLei Liu, Wei-Peng Chen
Life of this patent
Estimating the term…
Abstract
A method may include obtaining a machine learning (ML) pipeline including a plurality of functional blocks within the ML pipeline. The method may also include using the ML pipeline as an input to a visualization predictor, where the visualization predictor may be trained to output one or more visualization commands based on relationships between the visualization commands and the functional blocks within the pipeline. The method may additionally include invoking the visualization commands to instantiate the ML pipeline with visualizations generated by the one or more visualization commands.
Claims18
A method, comprising:
- obtaining a machine learning (ML) pipeline including a plurality of functional blocks within the ML pipeline;
- using the ML pipeline as an input to a visualization predictor, the visualization predictor trained to output one or more visualization commands based on relationships between the visualization commands and the functional blocks within the ML pipeline;
- instantiating the ML pipeline with the one or more visualization commands embedded within the ML pipeline;
- and
- generating the visualization predictor, generating the visualization predictor comprising:
- obtaining a plurality of training ML pipelines as a training dataset, each of the training ML pipelines including at least one visualization,
- determining first correlations between data features of precursor training datasets which are used to train the training ML pipelines and the at least one visualization,
- determining second correlations between code features of the training ML pipelines and the at least one visualization, and
- deriving a plurality of rules based on the first correlations and the second correlations, the rules providing a basis for predicting the visualization commands.
One or more non-transitory computer-readable media containing instructions which, when executed by one or more processors, cause a system to perform operations, the operations comprising:
- obtaining a machine learning (ML) pipeline including a plurality of functional blocks within the ML pipeline;
- using the ML pipeline as an input to a visualization predictor, the visualization predictor trained to output one or more visualization commands based on relationships between the visualization commands and the functional blocks within the ML pipeline;
- instantiating the ML pipeline with the one or more visualization commands embedded within the ML pipeline;
- and
- generating the visualization predictor, generating the visualization predictor comprising:
- obtaining a plurality of training ML pipelines as a training dataset, each of the training ML pipelines including at least one visualization;
- determining first correlations between data features of precursor training datasets which are used to train the training ML pipelines and the at least one visualization;
- determining second correlations between code features of the training ML pipelines and the at least one visualization;
- and
- deriving a plurality of rules based on the first correlations and the second correlations, the rules providing a basis for predicting the visualization commands.
Dependencies are inferred from the claim text.