Systems and methods for automatically serializing and deserializing models

1. A computer system for automatically generating models based on model-describing human-readable documents, the computer system including at least one processor in communication with at least one memory device, the at least one processor is programmed to:

store a first model, wherein the first model includes a plurality of functionalities;

generate a human-readable document based on the first model, wherein the human-readable document describes the first model such that a user is able to determine, via the human-readable document, how the first model performs the plurality of functionalities upon execution;

generate a second model based on the human-readable document, wherein the second model includes the plurality of functionalities;

train the second model;

generate a new human-readable document based on the trained second model, wherein the new human-readable document describes the trained second model such that the user is able to, via the new human-readable document, determine how the trained second model performs the plurality of functionalities upon execution; and

generate an updated second model based on the new human-readable document.

2. The computer system in accordance with claim 1 , wherein the at least one processor is further programmed to:

receive a plurality of data sets;

perform an execution of the second model for each of the plurality of data sets; and

train the second model based on the plurality of executions of the second model.

3. The computer system in accordance with claim 1 , wherein the at least one processor is further programmed to:

determine a change between the updated second model and the second model;

determine whether the change has exceeded a threshold of change; and

if the change has exceeded the threshold of change, initiate generation of the new human-readable document and the updated second model.

4. The computer system in accordance with claim 1 , wherein the at least one processor is further programmed to:

receive a user request to access a website; and

perform an execution of the second model based on the user request.

5. The computer system in accordance with claim 1 , wherein the at least one processor is further programmed to:

store the human-readable document in a web accessible location;

generate a link to the web accessible location; and

integrate the link into a website associated with the second model.

6. The computer system in accordance with claim 5 , wherein the at least one processor is further programmed to replace the human-readable document with the new human-readable document.

7. The computer system in accordance with claim 1 , wherein the at least one processor is further programmed to:

receive an initial plurality of data sets; and

train the first model based on the initial plurality of data sets.

8. The computer system in accordance with claim 1 , wherein the at least one processor is further programmed to:

receive a template; and

compare the template to at least one of the first model and the second model;

wherein at least one of:

to generate the human-readable document includes to generate the human-readable document based on the comparison, and

to generate the new human-readable document includes to generate the new human-readable document based on the comparison.

9. The computer system in accordance with claim 8 , wherein the template includes a plurality of tags, and wherein the at least one processor is further programmed to:

compare each tag of the plurality of tags to the first model to identify a feature of the first model that matches the corresponding tag; and

apply the feature to at least one location associated with the tag.

10. The computer system in accordance with claim 9 , wherein the to generate the second model includes to generate the second model based on the template and the human-readable document.

11. The computer system in accordance with claim 10 , wherein the at least one processor is further programmed to:

identify one or more features associated with tags based on the template; and

generate the second model to include the one or more features associated with tags and to exclude text not associated with tags.

12. The computer system in accordance with claim 1 , wherein the at least one processor is further programmed to:

store a plurality of templates, wherein each template is based on a different human-readable document; and

receive a user selection of a template of the plurality of templates;

wherein to generate the human-readable document includes to generate the human-readable document based on the user-selected template and the first model.

13. The computer system in accordance with claim 1 , wherein the at least one processor is further programmed to:

receive a plurality of data sets;

execute the first model based on the plurality of data sets to receive a first plurality of results;

execute the second model based on the plurality of data sets to receive a second plurality of results; and

compare the first plurality of results with the second plurality of results to determine an accuracy of the second model.

14. A computer-based method for automatically generating models based on model-describing human-readable documents, the method is implemented on a serialization/deserialization (“SD”) computer device including at least one processor in communication with at least one memory device, said method comprising:

storing, in the memory device, a first model, wherein the first model includes a plurality of functionalities;

generating, by the processor, a human-readable document based on the first model, wherein the human-readable document describes the first model such that a user is able to determine, via the human-readable document, how the first model performs the plurality of functionalities upon execution;

generating, by the processor, a second model based on the human-readable document, wherein the second model includes the plurality of functionalities; and

training the second model;

generating a new human-readable document based on the trained second model, wherein the new human-readable document describes the trained second model such that the user is able to, via the new human-readable document, determine how the trained second model performs the plurality of functionalities upon execution; and

generating an updated second model based on the new human-readable document.

15. The method of claim 14 , wherein training the second model further comprises:

receiving a plurality of data sets;

performing an execution of the second model for each of the plurality of data sets; and

training the second model based on the plurality of executions of the second model.

16. The method of claim 14 further comprising:

determining a change between the updated second model and the second model;

determining whether the change has exceeded a threshold of change; and

if the change trained second model has exceeded the threshold of change, initiating generation of the new human-readable document and the updated second model.

17. The method of claim 14 further comprising:

receiving a user request to access a website; and

performing an execution of the second model based on the user request.

18. The method of claim 14 further comprising:

storing the human-readable document in a web accessible location;

generating a link to the web accessible location; and

integrating the link into a website associated with the second model.

19. The method of claim 18 further comprises replacing the human-readable document with the new human-readable document.

20. The method of claim 14 further comprising:

receiving an initial plurality of data sets; and

training the first model based on the initial plurality of data sets.

21. The method of claim 14 further comprising:

receiving a template; and

comparing the template to the first model; and

wherein the generating the human-readable document includes generating the human-readable document based on the comparison.

22. The method of claim 21 , wherein the template includes a plurality of tags, and wherein said method further comprises:

comparing each tag of the plurality of tags to the first model to identify a feature of the first model that matches the corresponding tag; and

applying the feature to at least one location associated with the tag.

23. The method of claim 22 , wherein the generating the second model includes generating the second model based on the template and the human-readable document.

24. The method of claim 23 further comprising:

identifying one or more features associated with tags based on the template; and

generating the second model to include the one or more features associated with tags and to exclude text not associated with tags.

25. The method of claim 14 further comprising:

storing a plurality of templates, wherein each template is based on a different human-readable document; and

receiving a user selection of a template of the plurality of templates;

wherein the generating the human-readable document includes generating the human-readable document based on the user-selected template and the first model.

26. The method of claim 14 further comprising:

receiving a plurality of data sets;

executing the first model based on the plurality of data sets to receive a first plurality of results;

executing the second model based on the plurality of data sets to receive a second plurality of results; and

comparing the first plurality of results with the second plurality of results to determine an accuracy of the second model.

27. At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by at least one processor, the computer-executable instructions cause the processor to:

store a first model, wherein the first model includes a plurality of functionalities;

generate a human-readable document based on the first model, wherein the human-readable document describes the first model such that a user is able to determine, via the human-readable document, how the first model performs the plurality of functionalities upon execution;

generate a second model based on the human-readable document, wherein the second model includes the plurality of functionalities; and

train the second model;

generate a new human-readable document based on the trained second model, wherein the new human-readable document describes the trained second model such that the user is able to, via the new human-readable document, determine how the trained second model performs the plurality of functionalities upon execution; and

generate an updated second model based on the new human-readable document.

28. The computer-readable storage media of claim 27 , wherein the computer-executable instructions further cause the processor to:

receive a plurality of data sets;

perform an execution of the second model for each of the plurality of data sets; and

train the second model based on the plurality of executions of the second model.

29. The computer-readable storage media of claim 27 , wherein the computer-executable instructions further cause the processor to:

determine a change between the updated second model and the second model;

determine whether the change has exceeded a threshold of change; and

if the change has exceeded the threshold of change, initiate generation of the new human-readable document and the updated second model.

30. The computer-readable storage media of claim 27 , wherein the computer-executable instructions further cause the processor to:

receive a user request to access a website; and

perform an execution of the second model based on the user request.

31. The computer-readable storage media of claim 27 , wherein the computer-executable instructions further cause the processor to:

store the human-readable document in a web accessible location;

generate a link to the web accessible location; and

integrate the link into a website associated with the second model.

32. The computer-readable storage media of claim 31 , wherein the computer-executable instructions further cause the processor to replace the human-readable document with the new human-readable document.

33. The computer-readable storage media of claim 27 , wherein the computer-executable instructions further cause the processor to:

receive a plurality of data sets; and

train the first model based on the plurality of data sets.

34. The computer-readable storage media of claim 27 , wherein the computer-executable instructions further cause the processor to:

receive a template; and

compare the template to the first model;

wherein to generate the human-readable document includes to generate the human-readable document based on the comparison.

35. The computer-readable storage media of claim 34 , wherein the template includes a plurality of tags, and wherein the computer-executable instructions further cause the processor to:

compare each tag of the plurality of tags to the first model to identify a feature of the first model that matches the corresponding tag; and

apply the feature to at least one location associated with the tag.

36. The computer-readable storage media of claim 35 , wherein the to generate the second model includes to generate the second model based on the template and the human-readable document.

37. The computer-readable storage media of claim 35 , wherein the computer-executable instructions further cause the processor to:

identify one or more features associated with tags based on the template; and

generate the second model to include the one or more features associated with tags and to exclude text not associated with tags.

38. The computer-readable storage media of claim 27 , wherein the computer-executable instructions further cause the processor to:

store a plurality of templates, wherein each template is based on a different human-readable document; and

receive a user selection of a template of the plurality of templates;

wherein to generate the human-readable document includes to generate the human-readable document based on the user-selected template and the first model.

39. The computer-readable storage media of claim 27 , wherein the computer-executable instructions further cause the processor to:

receive a plurality of data sets;

execute the first model based on the plurality of data sets to receive a first plurality of results;

execute the second model based on the plurality of data sets to receive a second plurality of results; and

compare the first plurality of results with the second plurality of results to determine an accuracy of the second model.