Integration of model execution engine containers with a model development environment
An analytic model generated in a data scientist model acquisition toolbench is received. An analytic model is translated using a structuring tool. An execution simulation for testing the translated analytic model is performed for executing the model in the data scientist model acquisition toolbench.
1. A system, comprising:
a processor configured to:
receive an analytic model generated in a data scientist model acquisition toolbench comprising a computational notebook user interface comprising a cell with code;
display the analytic model in the computational notebook user interface to accept a smart comment within the cell;
translate an analytic model using a structuring tool based at least in part on the smart comment; and
perform an execution simulation for testing the translated analytic model for executing the model in the data scientist model acquisition toolbench; and
a memory coupled to the processor and configured to provide the processor with instructions.
2. The system of claim 1 , wherein the processor is further configured to export the translated analytic model for deployment in a production execution environment.
3. The system of claim 1 , wherein the processor is further configured to export the translated analytic model to a virtualized execution environment for an analytic engine.
4. The system of claim 1 , wherein the structuring tool is integrated into the data scientist model acquisition toolbench.
5. The system of claim 1 , wherein the execution simulator is integrated into the data scientist model acquisition toolbench.
6. The system of claim 1 , wherein model deploy tools are integrated into the data scientist model acquisition toolbench.
7. The system of claim 1 , wherein the processor is further configured to publish to export the analytic model to a model manager.
8. The system of claim 1 , wherein the processor is further configured to perform model life cycle tracking.
9. The system of claim 8 , wherein performing model life cycle tracking comprises using a graph database for tracking any changes to the model from development through deployment life cycle.
10. The system of claim 1 , wherein the processor is further configured to support a plug-in for a second data scientist model acquisition toolbench.
11. The system of claim 1 , wherein translating comprises an object-based abstraction of the analytic model.
12. The system of claim 1 , wherein the data scientist model acquisition toolbench comprises at least one of the following: Jupyter, Rstudio, Zeppelin, Cloudera, and Data Science Toolbench.
13. The system of claim 1 , wherein the data scientist model acquisition toolbench comprises tools for at least one of the following: execution simulation, automatic schema generation, structuring, and test.
14. The system of claim 1 , wherein the analytic model is written in at least one of the following: C, Python, Java, R, S, SAS, PFA, H2O, PMML, SPSS, and MATLAB.
15. The system of claim 1 , wherein translating includes using reasonable factoring.
16. The system of claim 1 , wherein translating includes using the smart comment.
17. The system of claim 1 , wherein translating includes using the smart comment for input schema or output schema.
18. The system of claim 1 , wherein translating includes using the smart comment for initializing code or ignoring code.
19. A method, comprising:
receiving an analytic model generated in a data scientist model acquisition toolbench comprising a computational notebook user interface comprising a cell with code;
displaying the analytic model in the computational notebook user interface to accept a smart comment within the cell;
translating an analytic model using a structuring tool based at least in part on the smart comment; and
performing an execution simulation for testing the translated analytic model for executing the model in the data scientist model acquisition toolbench.
20. A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:
receiving an analytic model generated in a data scientist model acquisition toolbench comprising a computational notebook user interface comprising a cell with code;
displaying the analytic model in the computational notebook user interface to accept a smart comment within the cell;
translating an analytic model using a structuring tool based at least in part on the smart comment; and
performing an execution simulation for testing the translated analytic model for executing the model in the data scientist model acquisition toolbench.