公开(公告)号：US11461351B1

公开(公告)日：2022-10-04

申请号：US17390883

申请日：2021-07-31

Applicant: Snowflake Inc.

Inventor： Simon A. Field ,  Stuart Ozer

IPC: G06F16/25 ,  G06F16/2455 ,  G06F16/84 ,  G06F16/22

Abstract: The subject technology receives raw input data from a source table, the raw input data including data comprising input features for a machine learning model, the raw input data being in a first format including at least multiple rows with each row including multiple columns of values. Based at least in part on the source table, the subject technology generates table metadata corresponding to the source table. Based at least in part on the received raw input data, the subject technology generates column metadata corresponding to values from the source table. The subject technology generates cell data for a feature store table based at least in part on the values from the source table. The subject technology performs at least one database operation to generate the feature store table including at least the generated table metadata, the generated column metadata, and the generated cell data.

公开(公告)号：US20230409589A1

公开(公告)日：2023-12-21

申请号：US18458425

申请日：2023-08-30

Applicant: Snowflake Inc.

Inventor： Simon A. Field ,  Stuart Ozer

IPC: G06F16/25 ,  G06F16/2455 ,  G06F16/84 ,  G06F16/22

CPC classification number: G06F16/25 ,  G06F16/24558 ,  G06F16/86 ,  G06F16/2282

Abstract: The subject technology generates, by a database system, cell data for a particular table based on values from a source table, the values being based on raw input data, the source table comprising multiple rows and multiple columns, the raw input data comprising values in a first format, the values comprising input features corresponding to datasets included in the raw input data for machine learning models, the source table being provided by an external environment, the external environment comprising an external system from the database system. The subject technology performs a database operation to generate the particular table including table metadata, column metadata, and the generated cell data, the generated particular table comprising a second format that causes more efficient processing of data by the database system using a single query on the particular table compared to processing the raw input data from the source table.

公开(公告)号：US20230004571A1

公开(公告)日：2023-01-05

申请号：US17899160

申请日：2022-08-30

Applicant: Snowflake Inc.

Inventor： Simon A. Field ,  Stuart Ozer

IPC: G06F16/25 ,  G06F16/84 ,  G06F16/22 ,  G06F16/2455

Abstract: The subject technology receives, by a database system, raw input data from a source table provided by a machine learning development environment, the source table comprising multiple rows where each row includes multiple columns, the raw input data comprising values in a first format, the values comprising input features corresponding to datasets included in the raw input data for machine learning models, the machine learning development environment comprising an external system from the database system and is accessed by a plurality of different users that are external to the database system. The subject technology generates cell data for a feature store table based at least in part on the values from the source table. The subject technology performs at least one database operation to generate the feature store table including at least table metadata, column metadata, and the generated cell data.

公开(公告)号：US11501015B2

公开(公告)日：2022-11-15

申请号：US17644732

申请日：2021-12-16

Applicant: Snowflake Inc.

Inventor： Monica J. Holboke ,  Justin Langseth ,  Stuart Ozer ,  William L. Stratton, Jr.

IPC: G06F21/62 ,  G06F16/28 ,  G06N20/00 ,  G06K9/62 ,  G06F16/25

Abstract: A secure machine learning system of a database system can be implemented to use secure shared data to train a machine learning model. To manage the model, a first user of the database can share data in an encrypted view with a second user of the database, and further share one or more functions of an application that accesses the data while the data is encrypted. The second user can access functions of the application and can call the functions to generate a trained machine learning model and further generate machine learning outputs (e.g., predictions) from the trained model.