Automatic Retraining Of Machine Learning Models Upon Data Deletion

1 . A method, comprising:

identifying a first data set including personal data, wherein the first data set is used to train a machine learning model;

deleting the personal data from a data store associated with the machine learning model; and

retraining, based on deleting the personal data, the machine learning model using a second data set that excludes the personal data.

2 . The method of claim 1 , further comprising:

determining a set of data attributes associated with the first data set; and

selecting the second data set based on the set of data attributes.

3 . The method of claim 1 , further comprising:

determining a set of data attributes associated with the first data set;

identifying a subset of the second data set based on the set of data attributes;

determining that the subset satisfies a quality condition; and

generating the second data set based on determining that the subset satisfies the quality condition.

4 . The method of claim 1 , further comprising:

determining a set of data attributes associated with the first data set;

identifying a subset of the second data set based on the set of data attributes;

determining that the subset fails to satisfy a quality condition; and

providing, to a telemetry service and based on determining that the subset fails to satisfy the quality condition, a telemetry request for a collection of telemetry data.

5 . The method of claim 1 , further comprising:

determining a set of data dependencies associated with the first data set; and

modifying the set of data dependencies based on deleting the personal data.

6 . The method of claim 1 , further comprising:

determining a set of data attributes associated with the first data set;

identifying a subset of the second data set based on the set of data attributes;

determining that the subset fails to satisfy a quality condition; and

modifying the machine learning model based on determining that the subset fails to satisfy the quality condition.

7 . The method of claim 1 , further comprising:

determining a set of data attributes associated with the first data set;

determining at least one of a set of data dependencies associated with the first data set or a set of two or more machine learning models, including the machine learning model, associated with the first data set; and

modifying at least one of the set of data dependencies or the set of two or more machine learning models.

8 . The method of claim 1 , further comprising:

receiving a request to delete the personal data, wherein deleting the personal data comprises deleting the personal data based on the request; and

outputting, for display, an indication that the personal data was deleted and an indication associated with the retraining of the machine learning model.

9 . A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising:

identifying a first data set including personal data, wherein the first data set is used to train a machine learning model;

deleting the personal data from a data store associated with the machine learning model; and

retraining, based on deleting the personal data, the machine learning model using a second data set that excludes the personal data.

10 . The non-transitory computer readable medium of claim 9 , the operations further comprising:

generating a data map indicative of a lineage associated with the personal data; and

updating the data map based on deleting the personal data.

11 . The non-transitory computer readable medium of claim 9 , the operations further comprising:

determining, based on a data map, a set of data attributes associated with the first data set; and

identifying, based on the data map and the set of data attributes, the second data set.

12 . The non-transitory computer readable medium of claim 9 , the operations further comprising:

determining a set of data attributes associated with the first data set;

identifying a subset of the second data set based on the set of data attributes;

determining that the subset satisfies a quality condition; and

determining that the subset satisfies a quantity condition, wherein the second data set is the subset.

13 . The non-transitory computer readable medium of claim 9 , the operations further comprising:

determining a set of data attributes associated with the first data set;

identifying a subset of the second data set based on the set of data attributes;

determining that the subset satisfies a quality condition;

determining that the subset fails to satisfy a quantity condition;

identifying an additional subset of the second data set based on the set of data attributes; and

generating the second data set by combining the subset with the additional subset.

14 . The non-transitory computer readable medium of claim 9 , wherein the first data set consists of the personal data and remaining data, and wherein the second data set consists of the remaining data.

15 . The non-transitory computer readable medium of claim 9 , the operations further comprising:

receiving a request to delete the personal data; and

identifying the machine learning model based on the request and a data map.

16 . A system, comprising:

a memory subsystem storing instructions; and

processing circuitry configured to execute the instructions to cause the system to:

identify a first data set including personal data, wherein the first data set is used to train a machine learning model;

delete the personal data from a data store associated with the machine learning model; and

retrain, based on deleting the personal data, the machine learning model using a second data set that excludes the personal data.

17 . The system of claim 16 , wherein the processing circuitry is configured to execute the instructions to further cause the system to:

update a data map based on deleting the personal data.

18 . The system of claim 16 , wherein the processing circuitry is configured to execute the instructions to further cause the system to:

identify an additional machine learning model trained using the personal data; and

retraining, based on deleting the personal data, the additional machine learning model using a third data set that excludes the personal data.

19 . The system of claim 16 , wherein the processing circuitry is configured to execute the instructions to further cause the system to:

determining, based on a data map, a set of data attributes associated with the first data set;

identifying, based on the data map and the set of data attributes, the second data set; and

determining that the second data set excludes additional personal data, wherein retraining the machine learning model comprises:

retraining the machine learning model based on determining that the second data set excludes additional personal data.

20 . The system of claim 16 , wherein the processing circuitry is configured to execute the instructions to further cause the system to:

receive, from a user device, a request to delete the personal data;

identify the machine learning model based on the request; and

output, for display at the user device, a delete notification indicative of deletion of the personal data.