公开(公告)号：US20240104394A1

公开(公告)日：2024-03-28

申请号：US18012387

申请日：2022-03-11

Applicant: Google LLC

Inventor： Amy Skerry-Ryan ,  Quentin Lascombes de Laroussilhe ,  Ronald Rong Yang ,  Carla Marie Riggi ,  Chansoo Lee ,  Jordan Arthur Grimstad ,  Christopher Mark Lamb ,  Joseph Michael Moran ,  Nihesh Anderson Klutto Milleth ,  Noah Weston Hadfield-Menell ,  Volodymyr Shtenovych ,  Ziqi Huang ,  Sagi Perel ,  Michael David Gerard ,  Mehadi Seid Hassen

IPC: G06N3/10 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06N3/10 ,  G06N3/045 ,  G06N3/08

Abstract: Provided are computing systems, methods, and platforms that automatically produce production-ready machine learning models and deployment pipelines from minimal input information such as a raw training dataset. In particular, one example computing system can import a training dataset associated with a user. The computing system can execute an origination machine learning pipeline to perform a model architecture search that selects and trains a machine learning model for the training dataset. Execution of the origination machine learning pipeline can also result in generation of a deployment machine learning pipeline configured to enable deployment of the machine learning model (e.g., running the machine learning model to produce inferences and/or optionally other tasks such as re-training and/or re-tuning the model). The computing system can export the machine learning model and the deployment machine learning pipeline for deployment of the machine learning model with the deployment machine learning pipeline

公开(公告)号：US20230401451A1

公开(公告)日：2023-12-14

申请号：US18199886

申请日：2023-05-19

Applicant: Google LLC

Inventor： Yutian Chen ,  Xingyou Song ,  Chansoo Lee ,  Zi Wang ,  Qiuyi Zhang ,  David Martin Dohan ,  Sagi Perel ,  Joao Ferdinando Gomes de Freitas

IPC: G06N3/0985 ,  G06N3/0455

CPC classification number: G06N3/0985 ,  G06N3/0455

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes receiving metadata for the training, generating a metadata sequence that represents the metadata, at each of a plurality of iterations: generating one or more trials that each specify a respective value for each of a set of hyperparameters, comprising, for each trial: generating an input sequence for the iteration that comprises (i) the metadata sequence and (ii) for any earlier trials, a respective sequence that represents the respective values for the hyperparameters specified by the earlier trial and a measure of performance for the trial, and processing an input sequence for the trial that comprises the input sequence for the iteration using a sequence generation neural network to generate an output sequence that represents respective values for the hyperparameters.