Holistic Optimization for Accelerating Iterative Machine Learning

A great deal of time and computational resources may be used when developing a machine learning or other data processing workflow. This can be related to the need to re-compute the workflow in response to adjustments to the workflow parameters, in order to assess the benefit of such adjustments so as to develop a workflow that satisfies accuracy or other constraints. Embodiments herein provide time and computational savings by selectively storing and re-loading intermediate results of steps of a data processing workflow. For each step of the workflow, during execution, a decision is made whether to store the intermediate results of the step. Thus, these embodiments can offer storage savings as well as processing speedups when repeatedly re-executing machine learning or other data processing workflows during workflow development.