Adaptive content deployment

1. A server based system, comprising:

a repository operable to store a plurality of web components, the web components including audio modules, text modules, web based application modules, video modules, image modules, and mapping modules;

a database operable to store identifiers of a plurality of users, the identifiers including Internet searches, website selections, user input information, device identifications, device locations, peripheral movements, and human-device interactions;

a machine learning module operable to generate a model of computing device interactions of the plurality of the users based on the stored identifiers of the plurality of users, to monitor a computing device interaction of a first of the plurality of users, to process the computing device interaction of the first user through the model, and to predict a format of web component delivery desired by the first user in a website based on the model; and

a formatter operable to retrieve a portion of the web components from the repository, and to automatically format the retrieved web components in the website for the first user based on the predicted format of web component delivery,

wherein the machine learning module is further operable to process the computing device interaction of the first user via supervised machine learning, unsupervised machine learning, or semi-supervised machine learning, and wherein the machine learning module is further operable to enhance a prediction of the format of web component delivery desired by the first user via said supervised machine learning, said unsupervised machine learning, or said semi-supervised machine learning of subsequent computing device interactions of the first user.

2. The system of claim 1 , wherein:

the machine learning module is further operable to update the model based on subsequent computing device interactions of the plurality of users.

3. The system of claim 1 , wherein:

the formatter is further operable to change the format of the web component delivery based on the enhanced prediction.

4. The system of claim 1 , further comprising:

a media distributer operable to deliver the formatted web components to the first user via a web-based application,

wherein the formatter is further operable to manipulate individual pixels of the formatted web components in the web-based application.

5. The system of claim 1 , further comprising:

an Extract, Transform, and Load (ETL) module that mines raw data of the users to parse out the stored identifiers of the users for subsequent processing via the machine learning module.

6. A method operable with a web server, comprising:

storing a plurality of web components in a repository, the web components including audio modules, text modules, web based application modules, video modules, image modules, and mapping modules;

storing identifiers of a plurality of users in a database, the identifiers including Internet searches, website selections, user input information, device identifications, device locations, peripheral movements, and human-device interactions;

machine learning a model of computing device interactions of the plurality of the users based on the stored identifiers of the plurality of users via supervised machine learning, unsupervised machine learning, or semi-supervised machine learning;

monitoring a computing device interaction of a first of the plurality of users;

processing the computing device interaction of the first user through the model;

predicting a format of web component delivery desired by the first user in a website based on the model;

retrieving a portion of the web components from the repository;

automatically formatting the retrieved web components in the website for the first user based on the predicted format of web component delivery; and

enhancing a prediction of the format of web component delivery desired by the user via said supervised machine learning, said unsupervised machine learning, or said semi-supervised machine learning of subsequent computing device interactions of the first user.

7. The method of claim 6 , further comprising:

updating the model based on subsequent computing device interactions of the plurality of users.

8. The method of claim 6 , further comprising:

changing the format of the web component delivery based on the enhanced prediction.

9. The method of claim 6 , further comprising:

manipulating individual pixels of the formatted web components in the web-based application.

10. The method of claim 6 , further comprising:

mining raw data of the users to parse out the stored identifiers of the users for subsequent processing via the machine learning module.

11. A non-transitory computer readable medium comprising instructions that, when executed by a processor, direct the processor to:

store a plurality of web components in a repository, the web components including audio modules, text modules, web based application modules, video modules, image modules, and mapping modules;

store identifiers of a plurality of users in a database, the identifiers including Internet searches, website selections, user input information, device identifications, device locations, peripheral movements, and human-device interactions;

machine learn a model of computing device interactions of the plurality of the users based on the stored identifiers of the plurality of users via supervised machine learning, unsupervised machine learning, or semi-supervised machine learning;

monitor a computing device interaction of a first of the plurality of users;

process the computing device interaction of the first user through the model;

predict a format of web component delivery desired by the first user in a website based on the model;

retrieve a portion of the web components from the repository;

automatically format the retrieved web components in the website for the first user based on the predicted format of web component delivery; and

enhance a prediction of the format of web component delivery desired by the user via said supervised machine learning, said unsupervised machine learning, or said semi-supervised machine learning of subsequent computing device interactions of the first user.

12. The non-transitory computer readable medium of claim 11 , further comprising instructions that direct the processor to:

update the model based on subsequent computing device interactions of the plurality of users.

13. The non-transitory computer readable medium of claim 11 , further comprising instructions that direct the processor to:

change the format of the web component delivery based on the enhanced prediction.

14. The non-transitory computer readable medium of claim 11 , further comprising instructions that direct the processor to:

manipulate individual pixels of the formatted web components in the web-based application.

15. The non-transitory computer readable medium of claim 11 , further comprising instructions that direct the processor to:

mine raw data of the users to parse out the stored identifiers of the users for subsequent processing via the machine learning module.