Guided drilldown framework for computer-implemented task definition

A method, implemented in a computing system comprising a memory and one or more processors, comprising:

• receiving a selection of a data model type, wherein data models of the data model type define a sequenced or hierarchical relationship of a plurality of data objects of a plurality of different data object types, the sequenced or hierarchical relationship differing between one or both of data model types or data models of a given data model type;
• retrieving a data model corresponding to the data model type;
• rendering a first display (1) requesting at least a first value to be assigned to a first data object of the plurality of data objects, the first data object having a first type, and (2) depicting the sequenced or hierarchical relationship of the plurality of data objects;
• rendering a second display (1) requesting at least a second value to be assigned to a second data object of the plurality of data objects, the second data object having a second type, and (2) depicting the sequenced or hierarchical relationship of the plurality of data objects;
• executing a computer-implemented process defined by the data model using the at least a first value and the at least a second value;
• and
• displaying execution results.

One or more tangible computer-readable storage media storing computer-executable instructions that, when executed by a computing system, cause the computing system to perform processing comprising:

• receiving a selection of a task level;
• determining a plurality of machine learning models stored in a repository that match the selected task level;
• receiving a selection of a machine learning problem type or a use case;
• determining at least one of the plurality of machine learning models that match the machine learning problem type or the use case, wherein machine learning models of the plurality of machine learning models comprise a plurality of components of a plurality of component types, where one or both of a number of components or component types differ between at least a portion of the machine learning models of the plurality of machine learning models;
• displaying a representation of at least one machine learning model of the determined at least one of the plurality of machine learning models, the representation illustrating a sequenced or hierarchical relationship between a plurality of components of the at least one machine learning model, wherein at least a first component of the plurality of components represents a data set to be processed and at least a second component of the plurality of components represents at least one algorithm to be used to process the data set;
• receiving input specifying the data set;
• processing the specified data set using the at least one algorithm;
• and
• providing executing results.