Machine learning model registry
Systems and methods to utilize a machine learning model registry are described. The system deploys a first version of a machine learning model and a first version of an access module to server machines. Each of the server machines utilizes the model and the access module to provide a prediction service. The system retrains the machine learning model to generate a second version. The system performs an acceptance test of the second version of the machine learning model to identify it as deployable. The system promotes the second version of the machine learning model by identifying the first version of the access module as being interoperable with the second version of the machine learning model and by automatically deploying the first version of the access module and the second version of the machine learning model to the plurality of server machines to provide the prediction service.
1. A system comprising:
at least one processor and memory having instructions that, when executed, cause the at least one processor to perform operations comprising:
automatically deploying a first version of an access module and a first version of a machine learning model to a plurality of machines to provide a prediction service;
generating a second version of the access module;
automatically identifying the second version of the access module as being interoperable with the first version of the machine learning model; and
automatically deploying the second version of the access module and the first version of the machine learning model to the plurality of machines to provide the prediction service.
2. The system of claim 1 , wherein the operations further comprise:
retraining the first version of the machine learning model to generate a second version of the machine learning model, wherein the second version of the machine learning model is a linear regression model.
3. The system of claim 2 , wherein the operations further comprise:
validating the second version of the machine learning model to identify the second version of the machine learning model as being deployable, wherein the validating the second version of the machine learning model is based on predetermined criteria.
4. The system of claim 3 , wherein the predetermined criteria includes a model evaluation metric.
5. The system of claim 4 , wherein the model evaluation metric includes a mean squared error metric.
6. The system of claim 3 , wherein the validating the second version of the machine learning model includes identifying that the second version of the access module and the second version of the machine learning model utilize common features, wherein the common features includes a number of features, wherein the access module includes a version identifier, a module name, and a deployable indicator, and wherein the deployable indicator indicates whether the version of the access module being identified by the version identifier is deployable.
7. The system of claim 6 , wherein the validating the second version of the machine learning model includes identifying the second version of the access module is interoperable with the second version of the machine learning model.
8. The system of claim 2 , wherein the operations further comprise:
presenting a user interface including a comparison of the first version of the machine learning model with the second version of the machine learning model, wherein the comparison is based on a model evaluation metric.
9. The system of claim 8 , wherein the operations further comprise:
receiving an identifier for identifying the second version of the machine learning model.
10. A method comprising:
automatically deploying a first version of an access module and a first version of a machine learning model to a plurality of machines to provide a prediction service;
generating a second version of the access module by utilizing at least one processor;
automatically identifying the second version of the access module as being interoperable with the first version of the machine learning model by utilizing at least one processor; and
automatically deploying the second version of the access module and the first version of the machine learning model to the plurality of machines to provide the prediction service.
11. The method of claim 10 , further comprising:
retraining the first version of the machine learning model to generate a second version of the machine learning model, wherein the second version of the machine learning model is a linear regression model.
12. The method of claim 11 , further comprising:
validating the second version of the machine learning model to identify the second version of the machine learning model as being deployable, wherein the validating the second version of the machine learning model is based on predetermined criteria.
13. The method of claim 12 , wherein the predetermined criteria includes a model evaluation metric.
14. The method of claim 13 , wherein the model evaluation metric includes a mean squared error metric.
15. The method of claim 12 , wherein the validating the second version of the machine learning model includes identifying the second version of the access module and the second version of the machine learning model as utilizing common features, wherein the common features includes a number of features, wherein the access module includes a version identifier, a module name, and a deployable indicator, and wherein the deployable indicator indicates whether the version of the access module being identified by the version identifier is deployable.
16. The method of claim 15 , wherein the validating the second version of the machine learning model includes identifying that the second version of the access module interoperates with the second version of the machine learning model.
17. The method of claim 11 , further comprising presenting a user interface including a comparison of the first version of the machine learning model with the second version of the machine learning model, wherein the comparison is based on a model evaluation metric.
18. The method of claim 17 , further comprising:
receiving an identifier for identifying the second version of the machine learning model.
19. A non-transitory machine-readable medium and storing a set of instructions that, when executed by a processor, causes a machine to perform operations comprising:
automatically deploying a first version of an access module and a first version of a machine learning model to a plurality of machines to provide a prediction service;
generating a second version of the access module by utilizing at least one processor;
automatically identifying the second version of the access module as being interoperable with the first version of the machine learning model by utilizing at least one processor; and
automatically deploying the second version of the access module and the first version of the machine learning model to the plurality of machines to provide the prediction service.
20. The non-transitory machine-readable medium of claim 19 , wherein the operations further comprise training the first version of the machine learning model to generate a second version of the machine learning model, wherein the second version of the machine learning model is a linear regression model.