SOURCING A NEW MACHINE-LEARNING PROJECT BY REUSING ARTIFACTS FROM REFERENCE MACHINE LEARNING PROJECTS

A computer-implemented method includes receiving a strategy associated with a new machine-learning (ML) project. There exist a plurality of ML projects, each of which includes artifacts, and for each such candidate project, the following are performed: iterations of the candidate ML project are divided into a first phase, including a first set of iterations, and a second phase, including a second set of iterations; a workload to generate the candidate ML project in the first phase is determined; a performance of the candidate ML project in the first phase is determined; an additional workload to generate the candidate ML project in the second phase is determined; and an increased performance of the candidate ML project in the second phase is determined. Final ML projects are selected from the candidate ML projects, based on the strategy. Artifacts of the final ML projects are incorporated into the new ML project.