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Sourcing a new machine-learning project by reusing artifacts from reference machine learning projects

机译:通过从参考机器学习项目中重用伪像来源新的机器学习项目

摘要

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.
机译:计算机实现的方法包括接收与新机器学习(ML)项目相关联的策略。 存在多个ML项目,每个ML项目包括伪影,并且对于每个这样的候选项目,执行以下内容:候选ML项目的迭代被划分为第一阶段,包括第一组迭代和第二阶段 ,包括第二组迭代; 确定在第一阶段生成候选ML项目的工作负载; 确定第一阶段中候选ML项目的性能; 确定额外的工作负载以在第二阶段生成候选ML项目; 确定了第二阶段中候选ML突出的增加的性能。 最终ML项目根据策略选自候选ML项目。 最终ML项目的文物纳入新的ML项目中。

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