A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The machine facilitates building, deploying, and/or training analytical models by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The machine can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The machine also provides a dynamically reconfigurable user interface for controlling the system.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: in a system architecture comprising model builder circuitry and model deployment circuitry: providing, by user interface circuitry, an analytical model control user interface to a user; building an analytical model by: receiving, by the user interface circuitry, a creation instruction entered via the user interface to have the model builder circuitry create an analytical model; accepting, by the user interface circuitry, an algorithm type selection of an analytical model algorithm type for the analytical model through the analytical model user interface; receiving, by the user interface circuitry, a training data designation of training data through the user interface circuitry; with the model builder circuitry, training the analytical model on the training data to create a trained analytical model; and storing the trained analytical model in a model storage database; and deploying the analytical model by: receiving, by the model deployment circuitry via the user interface circuitry, a deployment selection of the trained analytical model for deployment; retrieving, by the model deployment circuitry, the trained analytical model from the model storage database responsive to the deployment selection; determining, by the model deployment circuitry, a model code type of the trained analytical model; verifying, by the model deployment circuitry, that the trained analytical model conforms to pre-determined compatibility requirements applicable to the model code type; determining, by the model deployment circuitry, a compatible run time environment for the model code type from among multiple different available run time environments; deploying, by the model deployment circuitry, the trained analytical model on the compatible run time environment for processing incoming data and to generate run time results; and receiving, by a results storage database, the run time results from the compatible run time environment after deploying the trained analytical model on the compatible run time environment. 2. The method of claim 1 further comprising: receiving, by the model deployment circuitry, an instruction to deploy the trained analytical model on the compatible run time environment; converting, by the model deployment circuitry, the instruction to deploy the trained analytical model into a command-line argument for the compatible run time environment; and transmitting, via the communication interface, the command-line argument to the compatible run time environment. 3. (canceled) 4. The method of claim 1 further comprising: determining, by resource allocation circuitry, a resource load, an execution time, or both, for the trained analytical model using historical data for execution of the trained analytical model; determining by the resource allocation circuitry, a resource load capability of the compatible run time environment; determining by the resource allocation circuitry an execution frequency for the trained analytical model on the compatible run time environment based on the resource load, the execution time for the trained analytical model, the resource load capability of the compatible run time environment, or any combination thereof; and scheduling, by the resource allocation circuitry, an execution of the trained analytical model on the compatible run time environment at a frequency less than or equal to the execution frequency. 5. The method of claim 1 further comprising: determining, by the model deployment circuitry, that the trained analytical model includes dependencies supporting the model code type; and determining, by the model deployment circuitry, that the dependencies within the trained analytical model do not conflict. 6. The method of claim 1 further comprising: receiving, by the model deployment circuitry from the compatible run time environment, a first error rate for the trained analytical model; and outputting, by the model deployment circuitry, the first error rate via the communication interface. 7. The method of claim 6, wherein deploying the analytical model further comprises: comparing, by the model deployment circuitry, the first error rate with a second error rate of a second trained analytical model; determining, by the model deployment circuitry, that the first error rate is lower than the second error rate; and deploying, by the model deployment circuitry, the trained analytical model on the compatible run time environment instead of the second trained analytical model, responsive to determining that the first error rate is lower than the second error rate. 8. The method of claim 6 further comprising: comparing, by the model deployment circuitry, the first error rate with a second error rate of a second trained analytical model; determining, by the model deployment circuitry, that the second error rate is lower than the first error rate; and undeploying the trained analytical model and deploying the second trained analytical model on the compatible run time environment in response to determining that the second error rate is lower than the first error rate. 9. A machine comprising: user interface circuitry in communication with a run time environment, the user interface circuitry configured to: provide an analytical model control user interface; receive an instruction to have model builder circuitry create an analytical model through user interaction with the analytical model control user interface; accept a selection of the analytical model algorithm type for the analytical model through the analytical model control user interface; accept a designation of training data through the analytical model control user interface; and accept from the user a selection of a trained analytical model via the analytical model control user interface for deployment; a memory system configured to: store a trained analytical model in a model storage database; store run time results in a results storage database; the model builder circuitry configured to: receive the designation of training data from the user interface circuitry; train the analytical model on the training data to create a trained analytical model; and cause the trained analytical model to be stored in the model storage database; and model deployment circuitry configured to: receive the selection of the trained analytical model from the user interface circuitry; retrieve the trained analytical model from the model storage database; determine a model code type of the trained analytical model; verify that the trained analytical model conforms to pre-determined compatibility requirements applicable to the model code type; determine a compatible run time environment for the model code type from among multiple different available run time environments; deploy the trained analytical model on the compatible run time environment for processing incoming data and to generating run time results; and designate the results storage database as a location for storage of the run time results; and wherein the memory system is further configured to receive the run time results from the compatible run time environment after deploying the trained analytical model on the compatible run time environment. 10. The machine of claim 9 wherein the model deployment circuitry is further configured to: receive an instruction to deploy the trained analytical model on the compatible run time environment via the communication interface; convert the instruction to deploy the trained analytical model into a command-line argument for the compatible run time environment; and transmit, via the communication interface, the command-line argument to the compatible run time environment. 11. (canceled) 12. The machine of claim 9 further comprising resource allocation circuitry, the resource allocation circuitry configured to: determine a resource load, an execution time, or both, for the trained analytical model using historical data for execution of the trained analytical model; determine a resource load capability of the run time environment; determine an execution frequency for the trained analytical model on the run time environment based on the resource load, the execution time for the trained analytical model, the resource load capability of the run time environment, or any combination thereof; and schedule an execution of the trained analytical model on the run time environment at a frequency less than or equal to the execution frequency. 13. The machine of claim 9 wherein the model deployment circuitry is further configured to: determine that the trained analytical model includes dependencies supporting the model code type; and determine that the dependencies within the trained analytical model do not conflict. 14. The machine of claim 9 wherein the model deployment circuitry is further configured to: receive from the compatible run time environment a first error rate for the trained analytical model; and output the first error rate via the communication interface. 15. The machine of claim 14 wherein the model deployment circuitry is further configured to: compare the first error rate with a second error rate of a second trained analytical model; determine that the first error rate is lower than the second error rate; and deploy the trained analytical model on the compatible run time environment instead of the second trained analytical model, responsive to determining that the first error rate is lower than the second error rate. 16. The machine of claim 14 wherein the model deployment circuitry is further configured to: compare the first error rate with a second error rate of a second trained analytical model; determine that the second error rate is lower than the first error rate; and undeploy the trained analytical model and deploy the second trained analytical model on the compatible run time environment in response to determining that the second error rate is lower than the first error rate. 17. A method comprising: in a system architecture comprising model deployment circuitry: deploying a trained analytical model by: receiving, via a communication interface, an instruction to have model deployment circuitry access a trained analytical model; determining, by the model deployment circuitry, a model code type of the trained analytical model; verifying, by the model deployment circuitry, that the trained analytical model conforms to pre-determined compatibility requirements applicable to the model code type; determining, by the model deployment circuitry, that the trained analytical model includes all dependencies required for the model code type; determining, by the model deployment circuitry, that the dependencies within the trained analytical model do not conflict; determining, by the model deployment circuitry, a compatible run time environment for the model code type from among multiple different available run time environments; deploying, by the model deployment circuitry, the trained analytical model on the compatible run time environment for processing incoming data and generating run time results; and receiving, by a results storage database, the run time results from the compatible run time environment after deploying the trained analytical model on the compatible run time environment; outputting the run time results via the communication interface; receiving, by the model deployment circuitry from the compatible run time environment, a first error rate for the trained analytical model; outputting, by the model deployment circuitry, the first error rate via the communication interface; determining, by the model deployment circuitry, that the error rate indicates that the trained analytical model has become less accurate; and re-training the trained analytical model with training data in response to determining that the error rate indicates that the trained analytical model has become less accurate. 18. The method of claim 17 further comprising: receiving, by the model deployment circuitry, an instruction to deploy the trained analytical model on the compatible run time environment; converting, by the model deployment circuitry, the instruction to deploy the trained analytical model into a command-line instruction for the compatible run time environment; and transmitting, via the communication interface, the command-line instruction to the compatible run time environment. 19. The method of claim 17, wherein deploying the trained analytical model further comprises: comparing, by the model deployment circuitry, the first error rate with a second error rate of a second trained analytical model; determining, by the model deployment circuitry, that the first error rate is lower than the second error rate; and deploying, by the model deployment circuitry, the trained analytical model on the compatible run time environment instead of the second trained analytical model, responsive to determining that the first error rate is lower than the second error rate. 20. The method of claim 17 further comprising: comparing, by the model deployment circuitry, the first error rate with a second error rate of a second trained analytical model; determining, by the model deployment circuitry, that the second error rate is lower than the first error rate; and undeploying the trained analytical model and deploying the second trained analytical model on the compatible run time environment in response to determining that the second error rate is lower than the first error rate.