Automatically labeling functional blocks in pipelines of existing machine learning projects in a corpus adaptable for use in new machine learning projects

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:

• normalizing machine learning (ML) pipelines of existing ML projects stored in a corpus of existing ML projects;
• extracting functional blocks from the normalized ML pipelines;
• assigning a label to each of the functional blocks in the normalized ML pipelines;
• indexing each of the ML pipelines in the corpus based on the labels assigned to the functional blocks;
• and
• utilizing the labels assigned to the functional blocks in the corpus to generate a new pipeline to perform a new ML task on a new dataset of a new ML project.

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:
• normalizing machine learning (ML) pipelines of existing ML projects stored in a corpus of existing ML projects;
• extracting functional blocks from the normalized ML pipelines;
• assigning a label to each of the functional blocks in the normalized ML pipelines;
• indexing each of the ML pipelines in the corpus based on the labels assigned to the functional blocks;
• and
• utilizing the labels assigned to the functional blocks in the corpus to generate a new pipeline to perform a new ML task on a new dataset of a new ML project.