Data allocation with user interaction in a machine learning system
Various embodiments are provided for providing enhanced data allocation for machine learning operations in a computing environment by one or more processors in a computing system. One or more data sampling strategies may be determined based on a dataset. One or more enhanced training data allocations may be suggested for machine learning operations in a cloud computing environment based on the one or more data sampling strategies.
1 . A method for improving machine learning operations within a cloud computing environment when training a machine learning model for execution within the cloud computing environment, the method comprising:
determining one or more training data sampling strategies based on a dataset hosted using a cloud computing service in a cloud computing environment;
predicting a cost in resource utilization of each of the one or more training data sampling strategies in terms of a cost of data storage in a cloud object and in terms of a cost of training a machine learning model in the cloud computing environment to consume data provided by each of the one or more data sampling strategies;
predicting a degree of impact to accuracy of the one or more training data sampling strategies;
suggesting as part of a data pre-processing step of an automated machine learning model prediction service one or more enhanced training data allocations for machine learning operations in a cloud computing environment based on the one or more data sampling strategies, the one or more enhanced training data allocations each having one or more portions of data removed from the cloud computing environment to minimize cost of training the machine learning model while adhering to constraints of accuracy and run time; and
training the machine learning model utilizing the suggested enhanced training data allocations from the dataset, the machine learning model trained with the one or more portions of data removed from the dataset prior to training of the machine learning model to minimize the cost of training the machine learning model while adhering to the constraints of accuracy and run time.
2 . The method of claim 1 , further including receiving, as the dataset, a plurality of data types and data features, wherein the plurality of data types includes at least tabular data and timeseries data and the data features include at least a change point, seasonality data, and clustered data.
3 . The method of claim 1 , further including:
applying a forward allocation as a data sampling strategy for tabular data;
applying a backward allocation as a data sampling strategy for time series data;
applying a stratified sampling as a data sampling strategy for clustered data;
applying constraint sampling to include a defined time period as a data sampling strategy for seasonal data; and
using a change point detection as a data sampling strategy for abnormal data.
4 . The method of claim 1 , further including collecting feedback data based on the one or more data sampling strategies.
5 . The method of claim 1 , further including providing one or more t-shirt size options, data storage options, and the one or more data sampling strategies for suggesting one or more enhanced training data allocations.
6 . The method of claim 1 , further including providing a projected learning curve for the machine learning operations and benefit tradeoffs for each of the one or more enhanced training data allocations.
7 . The method of claim 1 , further including predicting a degree of impact on the dataset for each of the one or more enhanced training data allocations based on a training accuracy, training time, the dataset, computing hardware configurations, and the one or more data sampling strategies.
8 . A system for improving machine learning operations within a cloud computing environment when training a machine learning model for execution within the cloud computing environment, the system comprising:
one or more computers with executable instructions that when executed cause the system to:
determine one or more training data sampling strategies based on a dataset hosted using a cloud computing service in a cloud computing environment;
predicting a cost in resource utilization of each of the one or more training data sampling strategies in terms of a cost of data storage in a cloud object and in terms of a cost of training a machine learning model in the cloud computing environment to consume data provided by each of the one or more data sampling strategies;
predicting a degree of impact to accuracy of the one or more training data sampling strategies;
suggest as part of a data pre-processing step of an automated machine learning model prediction service one or more enhanced training data allocations for machine learning operations in a cloud computing environment based on the one or more data sampling strategies, the one or more enhanced training data allocations each having one or more portions of data removed from the cloud computing environment to minimize cost of training the machine learning model while adhering to constraints of accuracy and run time; and
training the machine learning model utilizing the suggested enhanced training data allocations from the dataset, the machine learning model trained with the one or more portions of data removed from the dataset prior to training of the machine learning model to minimize the cost of training the machine learning model while adhering to constraints of accuracy and run time.
9 . The system of claim 8 , wherein the executable instructions when executed cause the system to receive, as the dataset, a plurality of data types and data features, wherein the plurality of data types includes at least tabular data and timeseries data and the data features include at least a change point, seasonality data, and clustered data.
10 . The system of claim 8 , wherein the executable instructions when executed cause the system to:
apply a forward allocation as a data sampling strategy for tabular data;
apply a backward allocation as a data sampling strategy for time series data;
apply a stratified sampling as a data sampling strategy for clustered data;
apply constraint sampling to include a defined time period as a data sampling strategy for seasonal data; and
use a change point detection as a data sampling strategy for abnormal data.
11 . The system of claim 8 , wherein the executable instructions when executed cause the system to collect feedback data based on the one or more data sampling strategies.
12 . The system of claim 8 , wherein the executable instructions when executed cause the system to provide one or more t-shirt size options, data storage options, and the one or more data sampling strategies for suggesting one or more enhanced training data allocations.
13 . The system of claim 8 , wherein the executable instructions when executed cause the system to provide a projected learning curve for the machine learning operations and benefit tradeoffs for each of the one or more enhanced training data allocations.
14 . The system of claim 8 , wherein the executable instructions when executed cause the system to predict a degree of impact on the dataset for each of the one or more enhanced training data allocations based on a training accuracy, training time, the dataset, computing hardware configurations, and the one or more data sampling strategies.
15 . A computer program product for improving machine learning operations within a cloud computing environment when training a machine learning model for execution within the cloud computing environment, the computer program product comprising:
one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
program instructions to determine one or more training data sampling strategies based on a dataset hosted using a cloud computing service in a cloud computing environment;
program instructions to predict a cost in resource utilization of each of the one or more training data sampling strategies in terms of a cost of data storage in a cloud object and in terms of a cost of training a machine learning model in the cloud computing environment to consume data provided by each of the one or more data sampling strategies;
program instructions to suggest as part of a data pre-processing step of an automated machine learning model prediction service one or more enhanced training data allocations for machine learning operations in a cloud computing environment based on the one or more data sampling strategies, the one or more enhanced training data allocations each having one or more portions of data removed from the cloud computing environment to minimize cost of training the machine learning model while adhering to constraints of accuracy and run time; and
program instructions to train the machine learning model utilizing the suggested enhanced training data allocations from the dataset, the machine learning model trained with the one or more portions of data removed from the dataset prior to training of the machine learning model to minimize the cost of training the machine learning model while adhering to the constraints of accuracy and run time.
16 . The computer program product of claim 15 , further including program instructions to receive, as the dataset, a plurality of data types and data features, wherein the plurality of data types includes at least tabular data and timeseries data and the data features include at least a change point, seasonality data, and clustered data.
17 . The computer program product of claim 15 , further including program instructions to:
apply a forward allocation as a data sampling strategy for tabular data;
apply a backward allocation as a data sampling strategy for time series data;
apply a stratified sampling as a data sampling strategy for clustered data;
apply constraint sampling to include a defined time period as a data sampling strategy for seasonal data; and
use a change point detection as a data sampling strategy for abnormal data.
18 . The computer program product of claim 15 , further including program instructions to collect feedback data based on the one or more data sampling strategies.
19 . The computer program product of claim 15 , further including program instructions to:
provide one or more t-shirt size options, data storage options, and the one or more data sampling strategies for suggesting one or more enhanced training data allocations; and
provide a projected learning curve for the machine learning operations and benefit tradeoffs for each of the one or more enhanced training data allocations.
20 . The computer program product of claim 15 , further including program instructions to predict a degree of impact on the dataset for each of the one or more enhanced training data allocations based on a training accuracy, training time, the dataset, computing hardware configurations, and the one or more data sampling strategies.