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, to provide a prediction service, the system comprising:
at least one processor and memory having instructions that, when executed, cause the at least one processor to perform operations comprising:
training a first version of a machine learning model to generate a second version of the machine learning model, the first and second versions of the machine learning model being utilized to provide the prediction service, the first version of the machine learning model utilizing a first version of an access module to provide the prediction service;
automatically identifying the second version of the machine learning model as being interoperable with the first version of the access module; and
automatically deploying the first version of the access module and the second version of the machine learning model to a plurality of machines, each machine of the plurality of machines utilizing the first version of the access module and the second version of the machine learning model to provide the prediction service.
2. The system of claim 1 , wherein the second version of the machine learning model is a linear regression model.
3. The system of claim 1 , wherein the operations are 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.
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 the first version of the access module and the second version of the machine learning model utilize common features.
7. The system of claim 6 , wherein the validating the second version of the machine learning model includes identifying that the first version of the access module interoperates with the second version of the machine learning model.
8. The system of claim 1 , presenting a user interface including a comparison of the second version of the machine learning model with the first version of the machine learning model based on a model evaluation metric.
9. The system of claim 8 , further comprising receiving an identifier that identifies the second version of the machine learning model.
10. A method for providing a prediction service, the method comprising:
training a first version of a machine learning model to generate a second version of the machine learning model, the first and second versions of the machine learning model being utilized to provide the prediction service, the first version of the machine learning model utilizing a first version of an access module to provide the prediction service;
automatically identifying the second version of the machine learning model as being interoperable with the first version of the access module; and
automatically deploying the first version of the access module and the second version of the machine learning model to a plurality of machines, each machine of the plurality of machines utilizing the first version of the access module and the second version of the machine learning model to provide the prediction service.
11. The method of claim 10 , wherein the second version of the machine learning model is a linear regression model.
12. The method of claim 10 , 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 that the first version of the access module and the second version of the machine learning model utilize common features.
16. The method of claim 15 , wherein the validating the second version of the machine learning model includes identifying that the first version of the access module interoperates with the second version of the machine learning model.
17. The method of claim 10 , presenting a user interface including a comparison of the second version of the machine learning model with the first version of the machine learning model based on a model evaluation metric.
18. The method of claim 17 , further comprising receiving an identifier that identifies 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 to provide a prediction service, the operations comprising:
training a first version of a machine learning model to generate a second version of the machine learning model, the first and second versions of the machine learning model being utilized to provide the prediction service, the first version of the machine learning model utilizing a first version of an access module to provide the prediction service;
automatically identifying the second version of the machine learning model as being interoperable with the first version of the access module; and
automatically deploying the first version of the access module and the second version of the machine learning model to a plurality of machines, each machine of the plurality of machines utilizing the first version of the access module and the second version of the machine learning model to provide the prediction service.
20. The non-transitory machine-readable medium of claim 19 , wherein the second version of the machine learning model is a linear regression model.