AUTOMATED SELF-SUPERVISED MACHINE LEARNING SERVICES THROUGH UNIFIED MACHINE LEARNING ENABLERS

1 . A method for generating a chain of machine learning models comprising:

receiving a data sample comprising one or more features and a target property;

identifying, by a processor of a computer system, an unsupervised machine learning model trained to classify data samples based on the one or more features, independently of the target property, into a plurality of clusters;

classifying the data sample based on the one or more features using the unsupervised machine learning model to compute a cluster;

identifying, by the processor, a supervised machine learning model corresponding to the cluster; and

computing a value for the target property by supplying the data sample to the supervised machine learning model.

2 . The method of claim 1 , wherein the one or more features are selected from a plurality of fields of a data model, and

wherein the unsupervised machine learning model is identified from among a plurality of unsupervised machine learning models trained to classify data samples based on different combinations of fields of the data model.

3 . The method of claim 1 , wherein the supervised machine learning model comprises one or more selected from the group comprising:

a linear regression model;

a non-linear regression model; or

a classification model.

4 . The method of claim 1 , wherein the unsupervised machine learning model comprises a clustering model.

5 . The method of claim 1 , wherein the unsupervised machine learning model comprises an anomaly detection model.

6 . The method of claim 5 , further comprising supplying the value for the target property computed based on the supervised machine learning model to the anomaly detection model to compute a likelihood that the value for the target property is anomalous.

7 . The method of claim 1 , further comprising:

receiving a second data sample comprising one or more features and a second target property, the second target property being different from the target property of the data sample;

classifying the second data sample based on the one or more features of the second data sample using the unsupervised machine learning model to compute a second cluster;

identifying, by the processor, a second supervised machine learning model corresponding to the cluster; and

computing a second value for the second target property by supplying the second data sample to the second supervised machine learning model.

8 . The method of claim 1 , wherein the supervised machine learning model computes the cluster based on a first subset of the one or more features of the data sample, and

wherein the supervised machine learning model computes the value for the target property based on a second subset of the one or more features of the data sample, the second subset being different from the first subset.

9 . A system comprising:

a processor; and

a memory storing instructions that, when executed by the processor, cause the processor to:

receive a data sample comprising one or more features and a target property;

identify a first machine learning model trained to classify data samples based on the one or more features, independently of the target property, into a plurality of clusters;

classify the data sample based on the one or more features using the first machine learning model to compute a cluster;

identify a second machine learning model corresponding to the cluster; and

compute a value for the target property by supplying the data sample to the second machine learning model.

10 . The system of claim 9 , wherein the one or more features are selected from a plurality of fields of a data model, and

wherein the first machine learning model is identified from among a first plurality of machine learning models trained to classify data samples based on different combinations of fields of the data model.

11 . The system of claim 9 , wherein the second machine learning model comprises one or more selected from the group comprising:

a linear regression model;

a non-linear regression model; or

a classification model.

12 . The system of claim 9 , wherein the first machine learning model comprises a clustering model.

13 . The system of claim 9 , wherein the first machine learning model comprises an anomaly detection model.

14 . The system of claim 13 , wherein the memory further stores instructions that, when executed by the processor, cause the processor to supply the value for the target property computed based on the second machine learning model to the anomaly detection model to compute a likelihood that the value for the target property is anomalous.

15 . The system of claim 9 , wherein the memory further stores instructions that, when executed by the processor, cause the processor to:

receive a second data sample comprising one or more features and a second target property, the second target property being different from the target property of the data sample;

classify the second data sample based on the one or more features of the second data sample using the first machine learning model to compute a second cluster;

identify a third machine learning model corresponding to the cluster; and

compute a second value for the second target property by supplying the second data sample to the third machine learning model.

16 . The system of claim 9 , wherein the second machine learning model computes the cluster based on a first subset of the one or more features of the data sample, and

wherein the second machine learning model computes the value for the target property based on a second subset of the one or more features of the data sample, the second subset being different from the first subset.

17 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to:

receive a data sample comprising one or more features and a target property;

identify a first machine learning model trained to classify data samples based on the one or more features, independently of the target property, into a plurality of clusters;

classify the data sample based on the one or more features using the first machine learning model to compute a cluster;

identify a second machine learning model corresponding to the cluster;

compute an intermediate value by supplying the data sample to the second machine learning model;

identify a third machine learning model corresponding to the cluster; and

compute a value for the target property by supplying the data sample and the intermediate value to the third machine learning model.

18 . The non-transitory computer-readable medium of claim 17 , wherein a value corresponding to the intermediate value is absent from the data sample.

19 . The non-transitory computer-readable medium of claim 17 , further comprising instructions that, when executed by the processor, cause the processor to:

receive a second data sample comprising one or more features different from the one or more features of the data sample and a second target property, the second target property being different from the target property of the data sample;

classify the second data sample based on the one or more features of the second data sample using a fourth machine learning model different from the first machine learning model to compute a second cluster;

identify a fifth machine learning model corresponding to the cluster; and

compute a value for the second target property by supplying the second data sample to the fifth machine learning model.

20 . The non-transitory computer-readable medium of claim 17 , further comprising instructions that, when executed by the processor, cause the processor to:

receive a third data sample comprising one or more features;

identify the first machine learning model trained to classify data samples based on the one or more features into the plurality of clusters; and

detect an anomaly in the data sample based on the one or more features using the first machine learning model.