Network-based machine learning microservice platform

Abstract

A method may include a processing system having at least one processor for receiving a first machine learning model, the first machine learning model in a first format associated with a first development environment, adapting the first machine learning model to a containerized environment, validating the first machine learning model according to at least one validation criterion associated with a repository, and publishing the first machine learning model to the repository.

What is claimed is: 
     
         1 . A method comprising:
 receiving, by a processing system including at least one processor, a first machine learning model, wherein the first machine learning model is in a first format associated with a first development environment;   adapting, by the processing system, the first machine learning model to a containerized environment;   validating, by the processing system, the first machine learning model according to at least one validation criterion associated with a repository; and   publishing, by the processing system, the first machine learning model to the repository.   
     
     
         2 . The method of  claim 1 , wherein the at least one validation criterion comprises:
 an outcome of an application of a test data set to the first machine learning model in a simulation.   
     
     
         3 . The method of  claim 1 , further comprising:
 training the first machine learning model with a training data set.   
     
     
         4 . The method of  claim 1 , further comprising:
 validating a composite solution including the first machine learning model according to the at least one validation criterion associated with the repository.   
     
     
         5 . The method of  claim 4 , wherein the validating the composite solution comprises:
 determining a compatibility of at least a first artifact associated with the first machine learning model with at least a second artifact, wherein the first machine learning model comprises a first process of the composite solution, wherein the at least the second artifact is associated with a second process of the composite solution that is stored in the repository and is compatible with the containerized environment.   
     
     
         6 . The method of  claim 5 , wherein the at least the first artifact defines the compatibility of at least one of: an input port or an output port of the first machine learning model with at least one of: an input port or an output port of the second process. 
     
     
         7 . The method of  claim 4 , further comprising:
 training the composite solution with a training data set.   
     
     
         8 . The method of  claim 4 , wherein the composite solution is received from a user workstation. 
     
     
         9 . The method of  claim 4 , further comprising:
 deploying the composite solution to process a data stream in a network.   
     
     
         10 . The method of  claim 4 , further comprising:
 publishing the composite solution to the repository.   
     
     
         11 . The method of  claim 1 , wherein the first machine learning model is published to the repository as a microservice. 
     
     
         12 . The method of  claim 11 , wherein the microservice comprises an executable package comprising a set of artifacts to enable a performance of a data processing task, the set of artifacts including:
 at least one script defining the first machine learning model;   at least a first library associated with the first development environment;   configuration data for the first machine learning model; and   external compatibility information for at least one of an input port or an output port of the first machine learning model.   
     
     
         13 . The method of  claim 12 , wherein the set of artifacts further includes:
 at least a second library associated with the containerized environment.   
     
     
         14 . The method of  claim 1 , wherein the repository includes a search function for searching for at least one microservice stored in the repository based upon at least one of:
 a topic;   a popularity;   a ranking based upon at least one performance metric; or   an author.   
     
     
         15 . The method of  claim 1 , wherein the repository includes a search function for searching for at least one microservice stored in the repository based a type of function. 
     
     
         16 . The method of  claim 14 , wherein each of the at least one microservice comprises an executable package generated from one of a plurality of machine learning models. 
     
     
         17 . The method of  claim 14 , wherein each of the at least one microservice comprises a non-machine learning model-based executable package. 
     
     
         18 . The method of  claim 14 , wherein each of the at least one microservice comprises an executable package generated from a composite solution comprising at least one other artifact. 
     
     
         19 . A non-transitory computer-readable storage medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising:
 receiving a first machine learning model, wherein the first machine learning model is in a first format associated with a first development environment;   adapting the first machine learning model to a containerized environment;   validating the first machine learning model according to at least one validation criterion associated with a repository; and   publishing the first machine learning model to the repository.   
     
     
         20 . A device comprising:
 a processing system including at least one processor; and   a computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising:
 receiving a first machine learning model, wherein the first machine learning model is in a first format associated with a first development environment; 
 adapting the first machine learning model to a containerized environment; 
 validating the first machine learning model according to at least one validation criterion associated with a repository; and 
 publishing the first machine learning model to the repository.