Techniques for intuitive machine learning development and optimization
Various embodiments are generally directed to techniques for intuitive machine learning (ML) development and optimization, such as for application in a content services platform (CSP), for instance. Many embodiments include a ML model developer and a ML model evaluator to provide a graphical user interface that guides ML layman in developing, evaluating, implementing, managing, and/or optimizing ML models. Some embodiments are particularly directed to a common interface that provides a step-by-step user experience to develop and implement ML techniques. For example, embodiments may include computing a health score for various aspects of developing and/or optimizing ML models, and using the health score, and the factors contributing thereto, to guide production of a valuable ML model. These and other embodiments are described and claimed.
1. An apparatus, the apparatus comprising:
a processor; and
a memory comprising instructions that when executed by the processor cause the processor to:
present, via a graphical user interface (GUI), a plurality of data sets available for development of machine learning (ML) models, wherein each data set comprises a plurality of samples;
identify, based on input received via the GUI, a data set of the plurality of data sets for development of an ML model;
determine a plurality of characteristics associated with each sample in the data set identified, wherein a value for each of the plurality of characteristics is assignable to each sample in the data set identified;
compute a health of each characteristic in the plurality of characteristics, wherein health of a respective characteristic indicates a predictability of values for the respective characteristic;
present, via the GUI, the plurality of characteristics associated with the data set and the health of each of the plurality of characteristics;
identify, based on input received via the GUI, a target characteristic to predict values for;
determine a set of candidate characteristics based on the target characteristic;
compute a health of each candidate characteristic, wherein health of a respective characteristic indicates potential to be a predictor for the target characteristic; and
develop the ML model to predict values for the target characteristic.
2. The apparatus of claim 1 the memory comprising instructions that when executed by the processor cause the processor to:
determine an action to improve the health of a characteristic of the plurality of characteristics; and
present the action to improve the health of the characteristic via the GUI.
3. The apparatus of claim 2 , wherein the action to improve the health of the characteristic comprises one or more of adding samples to the data set identified and assigning values for the characteristic to one or more samples.
4. The apparatus of claim 1 , the memory comprising instructions that when executed by the processor cause the processor to compute the health of each characteristic in the plurality of characteristics with a ML algorithm.
5. The apparatus of claim 1 , the memory comprising instructions that when executed by the processor cause the processor to:
compute a health of each data set in the plurality of data sets, wherein health of a respective data set indicates a potential for training an accurate ML model based on the respective data set; and
present, via the GUI, the plurality of data sets available for development of machine learning (ML) models and the health of each of the plurality of data sets.
6. The apparatus of claim 5 , the memory comprising instructions that when executed by the processor cause the processor to:
determine an issue with the data set of the plurality of data sets to compute a health of the data set; and
present, via the GUI, the issue with the data set.
7. The apparatus of claim 6 , wherein the issue with the data set comprises insufficient samples in the data set.
8. The apparatus of claim 1 , the memory comprising instructions that when executed by the processor cause the processor to:
identify, based on input received via the GUI, a set of predictor characteristics to utilize in prediction of values for the target characteristic; and
develop the ML model to predict values for the target characteristic based on values for the set of predictor characteristics.
9. The apparatus of claim 8 , the memory comprising instructions that when executed by the processor cause the processor to:
determine an action to improve the health of a candidate characteristic in the set of candidate characteristics; and
present the action to improve the health of the candidate characteristic via the GUI.
10. The apparatus of claim 9 , wherein the action to improve the health of the characteristic comprises assigning values for the characteristic to one or more samples.
11. At least one non-transitory computer-readable medium comprising a set of instructions that, in response to being executed by a processor circuit, cause the processor circuit to:
present, via a graphical user interface (GUI), a plurality of data sets available for development of machine learning (ML) models, wherein each data set comprises a plurality of samples;
identify, based on input received via the GUI, a data set of the plurality of data sets for development of an ML model;
determine a plurality of characteristics associated with each sample in the data set identified, wherein a value for each of the plurality of characteristics is assignable to each sample in the data set identified;
compute a health of each characteristic in the plurality of characteristics, wherein health of a respective characteristic indicates a predictability of values for the respective characteristic;
present, via the GUI, the plurality of characteristics associated with the data set and the health of each of the plurality of characteristics;
identify, based on input received via the GUI, a target characteristic to predict values for;
determine a set of candidate characteristics based on the target characteristic;
compute a health of each candidate characteristic, wherein health of a respective characteristic indicates potential to be a predictor for the target characteristic; and
develop the ML model to predict values for the target characteristic.
12. The at least one non-transitory computer-readable medium of claim 11 , comprising instructions that, in response to being executed by the processor circuit cause the processor circuit to:
determine an issue with a characteristic of the plurality of characteristics to compute a health of the characteristic; and
present the issue with the characteristic via the GUI.
13. The at least one non-transitory computer-readable medium of claim 12 , wherein the issue with the characteristic comprises one or more of insufficient samples in the data set and insufficient values in the samples for the characteristic.
14. The at least one non-transitory computer-readable medium of claim 13 , comprising instructions that, in response to being executed by the processor circuit cause the processor circuit to:
determine a possible solution to the issue with the characteristic; and
present the possible solution to the issue via the GUI.
15. The at least one non-transitory computer-readable medium of claim 14 , wherein the possible solution to the issue comprises one or more of adding samples to the data set identified and assigning values for the characteristic to one or more samples.
16. The at least one non-transitory computer-readable medium of claim 15 , comprising instructions that, in response to being executed by the processor circuit cause the processor circuit to present, via the GUI, a link to add samples to the data set identified or assign values for the characteristic to one or more samples.
17. A computer-implemented method, comprising:
presenting, via a graphical user interface (GUI), a plurality of data sets available for development of machine learning (ML) models, wherein each data set comprises a plurality of samples;
identifying, based on input received via the GUI, a data set of the plurality of data sets for development of an ML model;
determining a plurality of characteristics associated with each sample in the data set identified, wherein a value for each of the plurality of characteristics is assignable to each sample in the data set identified;
identifying, based on input received via the GUI, a target characteristic to predict values for;
determining a set of candidate characteristics based on the target characteristic;
computing a health of each candidate characteristic, wherein health of a respective candidate characteristic indicates potential to be a predictor for the target characteristic;
presenting, via the GUI, the set of candidate characteristics and the health of each of the candidate characteristics; and
computing a health of each characteristic in the plurality of characteristics, wherein health of a respective characteristic indicates a predictability of values for the respective characteristic.
18. The computer-implemented method of claim 17 , comprising:
presenting, via the GUI, the plurality of characteristics associated with the data set and the health of each of the plurality of characteristics; and
developing the ML model to predict values for the target characteristic.