Sap Se

Yi Qing Isaac New

Ruidan He

Daniel Hermann Richard Dahlmeier

Denny Jee King Gee

Anantharaman Ravi

Weile Chen

Sundeep Gullapudi

Abraham Sasmito Adibowo

Hang Guo

Haodan Yang

Wenya Wang

Kham Sian Mung

Mohamed Yusuf Abdul Gafoor

Methods, systems, and computer-readable storage media for providing a set of heuristics representative of training data that is to be used to process a ML model through a training pipeline, the training pipeline including multiple phases, determining a set of time estimates by providing the set of heuristics as input to a training heuristics model that provides the set of time estimates as output, each time estimate in the set of time estimates indicating an estimated duration of a respective phase of the training pipeline, receiving, during processing of the ML model through the training pipeline, progress data representative of a progress of processing of the ML model, determining a set of status estimates including a status estimate for each phase of the training pipeline based on the progress data, and transmitting the set of time estimates and the set of status estimates for display.

Adaptive training completion time and status for machine learning models

Described herein is a machine learning framework for facilitating engagements. In accordance with one aspect of the framework, a machine learning model is trained based on the training data. A recommendation associated with an opportunity record may then be generated using the trained machine learning model. Results of one or more actions performed in response to the recommendation may be collected and fed back to the machine learning model to be used as the training data.

Machine learning framework for facilitating engagements

Growing community of inventors

Singapore, Singapore

Yi Qing Isaac New