APPLIED MACHINE LEARNING PROTOTYPES FOR HYBRID CLOUD DATA PLATFORM AND APPROACHES TO DEVELOPING, PERSONALIZING, AND IMPLEMENTING THE SAME

APPLIED MACHINE LEARNING PROTOTYPES FOR HYBRID CLOUD DATA PLATFORM AND APPROACHES TO DEVELOPING, PERSONALIZING, AND IMPLEMENTING THE SAME

Development of machine learning models and applications tends to be iterative and complex, made even harder because most of the necessary tools are not built for the entire machine learning lifecycle. Introduced here is a data platform that is able to accelerate time-to-value by enabling users to ut...

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Bibliographische Detailangaben
Hauptverfasser: Hunt, Patrick David, Dibia, Victor Chukwuma, Tsai, Yi Hsun, Beck, Melanie, Fletcher, Jeffrey George, Gila, Ofek, Schaser, Jeanne, Sinha, Subhadeep, Reed, Andrew, Bleakley, Alex, Yabe, Yuya, Thomas, Sushil, Wallace, Christopher James
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Beschreibung
Zusammenfassung:Development of machine learning models and applications tends to be iterative and complex, made even harder because most of the necessary tools are not built for the entire machine learning lifecycle. Introduced here is a data platform that is able to accelerate time-to-value by enabling users to utilize applied machine learning prototypes ("AMPs") made by others. These AMPs may be extendable, by the data platform, to new datasets, allowing machine learning to be developed and deployed more rapidly.