Abstract: 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.

Abstract: The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.

Abstract: 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.