International Business Machines Corporation

Shashank Vijaykumar Vagarali

Mahadev Khapali

Yugandhra Rayanki

Santosh Hegde

Charan Acharya Chandrashekar

Hemant Singh

Prabhu S Padashetty

A method for receiving an end-user model access data set, deriving a plurality of patterns of actions typically performed by the end-user based on analysis of the end-user model access data set, and deriving a first model deployment protocol to automatically deploy selected ML models of the plurality of ML models for the end-user when the end-user works with ML models based on the plurality of patterns of actions.

Managing user machine learning (ML) models

A computer-implemented method for creating an auto-scaled predictive analytics model includes determining, via a processor, whether a queue size of a service master queue is greater than zero. Responsive to determining that the queue size is greater than zero, the processor fetches a count of requests in a plurality of requests in the service master queue and a type for each of the requests. The processor derives a value for time required for each of the requests and retrieves a number of available processing nodes based on the time required for each of the requests. The processor then auto-scales a processing node number responsive to determining that a total execution time for all of the requests in the plurality of requests exceeds a predetermined time value and outputs an auto-scaled predictive analytics model based on the processing node number and queue size.

Auto scaling a distributed predictive analytics system with machine learning

In an approach to deriving highly accurate models, one or more computer processors train a set of machine learning models utilizing a training set and a deep learning algorithm; generate one or more feedback data sets for each model in the set of trained models; rank each model in the set of trained models based on the generated feedback data sets; dynamically adjust one or more thresholds, that initiate a retraining or deployment of one or more ranked models, based, at least in part, on one or more production environment requirements; responsive to exceeding one or more adjusted thresholds, automatically deploy one or more ranked models to one or more deployment environments based, at least in part, on the ranking of the one or more trained models; responsive to not exceeding one or more adjusted thresholds, retrain each model in the set of trained models.

Intelligent retraining of deep learning models utilizing hyperparameter sets

A computer-implemented method includes identifying a mobile phone. The method includes identifying one or more input devices. The one or more input devices are associated with the mobile phone. The method includes collecting behavior information from the input devices. The method includes applying machine learning to the behavior information to yield a schedule.

Growing community of inventors

Bengaluru, India

Shashank Vijaykumar Vagarali