Automatically generating a pipeline of a new machine learning project from pipelines of existing machine learning projects stored in a corpus

One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system to perform operations, the operations comprising:

• storing existing machine learning (ML) projects in a corpus, the existing ML projects including ML pipelines with functional blocks;
• generating a search query for a new ML project based on a new dataset for the new ML project and a new ML task for the new ML project;
• searching through the existing ML projects stored in the corpus, based on the search query, for a set of existing ML projects;
• merging the ML pipelines of the set of existing ML projects to generate a new ML pipeline for the new ML project, the new ML pipeline having functional blocks that are taken from the ML pipelines of the set of existing ML projects;
• and
• adapting the functional blocks of the new ML pipeline for the new ML project to enable the new ML pipeline to be executed to perform the new ML task on the new dataset.

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed by the one or more processors, cause the system to perform operations, the operations comprising:
• storing existing machine learning (ML) projects in a corpus, the existing ML projects including ML pipelines with functional blocks;
• generating a search query for a new ML project based on a new dataset for the new ML project and a new ML task for the new ML project;
• searching through the existing ML projects stored in the corpus, based on the search query, for a set of existing ML projects;
• merging the ML pipelines of the set of existing ML projects to generate a new ML pipeline for the new ML project, the new ML pipeline having functional blocks that are taken from the ML pipelines of the set of existing ML projects;
• and
• adapting the functional blocks of the new ML pipeline for the new ML project to enable the new ML pipeline to be executed to perform the new ML task on the new dataset.