A computing device creates and stores a state of an analytic model. A first indicator of a dataset is received. A second indicator of an analytic model type of a plurality of analytic model types to train using the dataset is received. An analytic engine of an analytic model of the analytic model type is instantiated. The analytic model of the analytic model type is trained using the dataset and the instantiated analytic engine. A third indicator to save a state of the analytic model is received. The state of the trained analytic model is generated. The generated state of the trained analytic model is written to an output file. The generated state includes a reentry point name of a function of the analytic model type called to instantiate the trained analytic mode.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to: receive a first indicator of a dataset; receive a second indicator of an analytic model type of a plurality of analytic model types to train using the dataset; instantiate an analytic engine of an analytic model of the analytic model type; train the analytic model of the analytic model type using the dataset and the instantiated analytic engine; receive a third indicator to save a state of the analytic model; generate the state of the trained analytic model; and write the generated state of the trained analytic model to an output file, wherein the generated state includes a reentry point name of a function of the analytic model type called to instantiate the trained analytic model. 2. The non-transitory computer-readable medium of claim 1, wherein generating the state comprises serializing the state of the trained analytic model. 3. The non-transitory computer-readable medium of claim 2, wherein the serialized state is written to the output file in binary. 4. The non-transitory computer-readable medium of claim 3, wherein the serialized state is written to the output file as a continuous block of bytes. 5. The non-transitory computer-readable medium of claim 4, further comprising computer-readable instructions that, when executed by the computing device, cause the computing device to: write a tag directory to the output file, wherein the tag directory includes a unique tag for a state item, a state item offset value at which the state item is stored in the continuous block of bytes, and a state item length value that defines a length in bytes of the state item for each state item of a set of tagged items. 6. The non-transitory computer-readable medium of claim 5, wherein a public serializing engine writes the tag directory to the output file. 7. The non-transitory computer-readable medium of claim 2, wherein serializing the state comprises a first set of tagged items serialized by a public serializing engine and a second set of tagged items serialized by the instantiated analytic engine. 8. The non-transitory computer-readable medium of claim 7, wherein the first set of tagged items are public state items used for each analytic model type of the plurality of analytic model types. 9. The non-transitory computer-readable medium of claim 8, wherein the first set of tagged items include byte identifiers that include byte sequences of a plurality of data types. 10. The non-transitory computer-readable medium of claim 8, wherein the first set of tagged items include a name of the analytic model type. 11. The non-transitory computer-readable medium of claim 8, wherein the first set of tagged items include a procedure name of the instantiated analytic engine. 12. The non-transitory computer-readable medium of claim 8, wherein the first set of tagged items include an input variable list for the dataset. 13. The non-transitory computer-readable medium of claim 12, wherein the first set of tagged items include statistics for each input variable of the input variable list. 14. The non-transitory computer-readable medium of claim 8, wherein the first set of tagged items include an output variable list for the dataset. 15. The non-transitory computer-readable medium of claim 8, wherein the second set of tagged items are private state items selected based on the received second indicator. 16. The non-transitory computer-readable medium of claim 2, wherein serializing the state comprises: loading a public serializing engine; instantiating the public serializing engine; and serializing a first set of tagged items using the instantiated public serializing engine. 17. The non-transitory computer-readable medium of claim 16, wherein after instantiating the public serializing engine and before serializing the first set of tagged items, serializing the state further comprises: requesting, by the public serializing engine, values for the first set of tagged items from the instantiated analytic engine; and receiving, by the public serializing engine, the values for the first set of tagged items from the instantiated analytic engine. 18. The non-transitory computer-readable medium of claim 17, wherein after instantiating the public serializing engine and before serializing the first set of tagged items, serializing the state further comprises: receiving, by the instantiated analytic engine, a request to define values for the first set of tagged items; defining, by the instantiated analytic engine, values for the first set of tagged items; and sending, by the instantiated analytic engine, the defined values for the first set of tagged items to the public serializing engine. 19. The non-transitory computer-readable medium of claim 18, wherein after serializing the first set of tagged items, serializing the state further comprises: creating, by the instantiated analytic engine, the second set of tagged items; and returning, by the instantiated analytic engine, the created second set of tagged items to the public serializing engine. 20. The non-transitory computer-readable medium of claim 19, wherein after returning the created second set of tagged items, serializing the state further comprises: receiving, by the public serializing engine, the second set of tagged items; and serializing the received second set of tagged items using the instantiated public serializing engine. 21. The non-transitory computer-readable medium of claim 16, wherein the public serializing engine writes the generated state to the output file. 22. A computing device comprising: a processor; and a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, control the computing device to receive a first indicator of a dataset; receive a second indicator of an analytic model type of a plurality of analytic model types to train using the dataset; instantiate an analytic engine of an analytic model of the analytic model type; train the analytic model of the analytic model type using the dataset and the instantiated analytic engine; receive a third indicator to save a state of the analytic model; generate the state of the trained analytic model; and write the generated state of the trained analytic model to an output file, wherein the generated state includes a reentry point name of a function of the analytic model type called to instantiate the trained analytic mode. 23. The computing device of claim 22, wherein generating the state comprises serializing the state of the trained analytic model. 24. The computing device of claim 23, wherein the serialized state is written to the output file in binary. 25. The computing device of claim 24, wherein the serialized state is written to the output file as a continuous block of bytes. 26. The computing device of claim 25, further comprising computer-readable instructions that, when executed by the processor, control the computing device to: write a tag directory to the output file, wherein the tag directory includes a unique tag for a state item, a state item offset value at which the state item is stored in the continuous block of bytes, and a state item length value that defines a length in bytes of the state item for each state item of a set of tagged items. 27. A method of storing a state of an analytic model, the method comprising: receiving a first indicator of a dataset; receiving a second indicator of an analytic model type of a plurality of analytic model types to train using the dataset; instantiating, by a computing device, an analytic engine of an analytic model of the analytic model type; training, by the computing device, the analytic model of the analytic model type using the dataset and the instantiated analytic engine; receive a third indicator to save a state of the analytic model; generating, by the computing device, the state of the trained analytic model; and writing, by the computing device, the generated state of the trained analytic model to an output file, wherein the generated state includes a reentry point name of a function of the analytic model type called to instantiate the trained analytic mode. 28. The method of claim 27, wherein generating the state comprises serializing the state of the trained analytic model. 29. The method of claim 28, wherein the serialized state is written to the output file in binary. 30. The method of claim 29, wherein the serialized state is written to the output file as a continuous block of bytes.