International Business Machines Corporation

Voranouth Supadulya

John Dillon Eversman

Jing James Xu

Dakuo Wang

Jun Song Wang

Ji Hui Yang

Lin Ju

Bei Chen

Chuang Gan

Theodoros Salonidis

Si Er Han

Thanh Lam Hoang

Craig Reginald Tomlyn

Long H Vu

Dhavalkumar C Patel

Syed Yousaf Shah

Gregory Bramble

Arunima Chaudhary

Abel Nicolas Valente

Daniel Karl I Weidele

Alex Swain

Nicholas Mazzitelli

Jishuo Yang

Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.

Visualization of a model selection process in an automated model selection system

Embodiments for providing automated machine learning visualization. Machine learning tasks, transformers, and estimators may be received into one or more machine learning composition modules. The machine learning composition modules generate one or more machine learning models. A machine learning model pipeline is a sequence of transformers and estimators and an ensemble of machine learning pipelines are an ensemble of machine learning pipelines. A machine learning model pipeline, an ensemble of a plurality of machine learning model pipelines, or a combination thereof, along with corresponding metadata, may be generated using the machine learning composition modules. Metadata may be extracted from the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof. An interactive visualization graphical user interface of the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof, and the extracted metadata may be generated.

Automated artificial intelligence radial visualization

Embodiments for managing feature engineering with relational data are provided. A graphical user interface (GUI) that provides a user with the ability to upload a plurality of tables, select joins between the plurality of tables, and select keys for the joins is provided. Responsive to receiving user input indicative of selecting joins between the plurality of tables and selecting keys for the joins utilizing the GUI, the user selections are automatically validated and actions associated with at least some of the plurality of tables are dynamically performed based on the user selections. Information associated with the user selections and the validating is provided. The information includes a recommendation to link a third key in the at least some of the plurality of tables to a fourth key in the at least some of the plurality of tables.

Methods and systems for visual management of relational data

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Austin, TX, United States of America

Voranouth Supadulya