MECHANISMS FOR INTELLIGENT MACHINE LEARNING DATA PREPARATION SERVICE
Mechanisms for intelligent machine learning data preparation services are described herein. In one aspect, a method by an intelligent data preparation service can include: receiving configuration information from a service provider to configure the data preparation service; receiving, from a user, configuration information comprising output requirements for data processed by the data preparation service and for the user, combining data inputs into a dataset; generating metadata for a set of features of the dataset; analyzing the data inputs, the set of features, and the metadata to derive recommended data preparation operations; selecting another set of features that satisfy the output requirements of the user, based on the user configuration information, the service provider configuration, the feature metadata, dataset context information, and the recommended data preparation operations; and configuring the intelligent data preparation service to manipulate the dataset for machine learning (ML) applications.
1 . A method by a data preparation service, comprising:
receiving, from a client, configuration information and output requirements for a machine learning (ML) application;
transmitting, to the client, data preparation recommendations for the client based on the received configuration information and the received output requirements;
receiving, from the client, one or more data preparation requests comprising at least identifiers of input data for processing by the data preparation service;
generating dataset metadata and context information via processing the input data;
determining a set of configurations of one or more remaining functions of the data preparation service based on the generated dataset metadata and context information; and
causing the input data to be further processed by the one or more remaining functions of the data preparation service according to the set of configurations.
2 . The method of claim 1 , further comprising:
generating output data based on the further processing of the input data.
3 . The method of claim 1 , wherein the set of configurations comprise a data synthesizer operation, a data cleaning operation, a data transform operation, a data labeling operation, or a combination thereof.
4 . The method of claim 1 , wherein the determining the set of configurations is further based on the data preparation recommendations.
5 . The method of claim 1 , wherein the one or more functions comprise an analytics/ML function, a data synthesizer function, a data clean function, a data transform function, a data labeling function, a data output function, or a combination thereof.
6 . The method of claim 1 , further comprising:
modifying a set of initial configurations for the one or more remaining functions according to the set of configurations.
7 . The method of claim 1 , wherein the at least identifiers for the input data comprise the input data.
8 . The method of claim 1 , further comprising:
receiving, from the client, a response to the data preparation recommendations comprising permissions to enforce the data preparation recommendations.
9 . The method of claim 1 , further comprising:
determining the data preparation recommendations based on an identifier for the ML application, an identifier of the client, learned behavior for the ML application, learned behavior for the client, or a combination thereof.
10 . The method of claim 1 , wherein the dataset metadata comprises numerical or categorical representations of the input data, data formats and types of the input data, units of measurements for the input data, data enumerations for the input data, range of values for the input data, identification of data to specific industries of the input data, or a combination thereof.
11 . The method of claim 1 , wherein configuration and output requirements comprise a ML application type, a maximum input feature value, one or more required output features, a minimum number of data samples, a target feature for the ML application, a target algorithm or model for the ML application, an output percentage of dataset samples for training, validating, and testing purposes, a permission for automating a data preparation workflow, or a combination thereof.
12 . An apparatus for a data preparation service, the apparatus comprising circuitry configured to:
receive, from a client, configuration information and output requirements for a machine learning (ML) application;
transmit, to the client, data preparation recommendations for the client based on the received configuration information and the received output requirements;
receive, from the client, one or more data preparation requests comprising at least identifiers of input data for processing by the data preparation service;
generate dataset metadata and context information via processing the input data;
determine a set of configurations of one or more remaining functions of the data preparation service based on the generated dataset metadata and context information; and
cause the input data to be further processed by the one or more remaining functions of the data preparation service according to the set of configurations.
13 . The method of claim 12 , wherein the circuitry is further configured to:
generate output data based on the further processing of the input data.
14 . The apparatus of claim 12 , wherein the set of configurations comprise a data synthesizer operation, a data cleaning operation, a data transform operation, a data labeling operation, or a combination thereof.
15 . The apparats of claim 12 , wherein the determining the set of configurations is further based on the data preparation recommendations.
16 . The apparatus of claim 12 , wherein the one or more functions comprise an analytics/ML function, a data synthesizer function, a data clean function, a data transform function, a data labeling function, a data output function, or a combination thereof.
17 . The apparatus of claim 12 , wherein the circuitry is further configured to:
modify a set of initial configurations for the one or more remaining functions according to the set of configurations.
18 . The apparatus of claim 12 , wherein the at least identifiers for the input data comprise the input data.
19 . The apparatus of claim 12 , wherein the circuitry is further configured to:
receive, from the client, a response to the data preparation recommendations comprising permissions to enforce the data preparation recommendations.
20 . A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a processor, cause a data preparation service to:
receive, from a client, configuration information and output requirements for a machine learning (ML) application;
transmit, to the client, data preparation recommendations for the client based on the received configuration information and the received output requirements;
receive, from the client, one or more data preparation requests comprising at least identifiers of input data for processing by the data preparation service;
generate dataset metadata and context information via processing the input data;
determine a set of configurations of one or more remaining functions of the data preparation service based on the generated dataset metadata and context information; and
cause the input data to be further processed by the one or more remaining functions of the data preparation service according to the set of configurations.