Machine learning deployment in radio access networks

Abstract

The described technology is generally directed towards machine learning deployment in radio access networks. A machine learning deployment pipeline can comprise a machine learning model design platform, a network automation platform, and a radio access network. Machine learning models can be designed at the machine learning model design platform, trained at the network automation platform, and deployed and used at the radio access network. The technology includes operations performed at each stage of the deployment pipeline in order to deploy machine learning models.

What is claimed is: 
     
         1 . A method, comprising:
 deploying, by a system comprising a processor, data representative of a trained machine learning model to radio access network equipment, wherein the trained machine learning model is usable by the radio access network equipment to analyze radio access network data and make radio access network control decisions based on the radio access network data;   receiving, by the system, performance feedback data representative of performance feedback related to operation of the trained machine learning model at the radio access network equipment; and   using, by the system, the performance feedback data to evaluate a performance of the trained machine learning model and identify an update for the trained machine learning model.   
     
     
         2 . The method of  claim 1 , further comprising training, by the system, an untrained machine learning model resulting in the data representative of the trained machine learning model. 
     
     
         3 . The method of  claim 2 , further comprising collecting, by the system, model training data from the radio access network equipment, wherein the model training data is for use in connection with training the untrained machine learning model. 
     
     
         4 . The method of  claim 2 , wherein the training comprises training the untrained machine learning model in a virtualized environment. 
     
     
         5 . The method of  claim 4 , wherein the virtualized environment comprises a virtualized radio access network command interface. 
     
     
         6 . The method of  claim 2 , wherein the training comprises multiple training cycles. 
     
     
         7 . The method of  claim 1 , further comprising modifying, by the system, the data representative of the trained machine learning model by incorporating the update in the trained machine learning model, resulting in updated model data representative of an updated machine learning model. 
     
     
         8 . The method of  claim 7 , further comprising training, by the system, the updated model data representative of the updated machine learning model. 
     
     
         9 . The method of  claim 8 , further comprising deploying, by the system, the updated model data representative of the updated machine learning model to the radio access network equipment. 
     
     
         10 . The method of  claim 9 , further comprising uploading, by the system, the updated model data representative of the updated machine learning model to a machine learning model design platform, and, in response to uploading the updated model data, receiving, by the system, published updated model data representative of a published updated machine learning model related to the updated machine learning model. 
     
     
         11 . The method of  claim 1 , further comprising:
 uploading, by the system, the data representative of the trained machine learning model to a machine learning model design platform; and   in response to the uploading, receiving, by the system, published model data representative of a published trained machine learning model, wherein the published model data is deployed to the radio access network equipment.   
     
     
         12 . The method of  claim 1 , wherein the trained machine learning model comprises a micro-service. 
     
     
         13 . Network automation platform equipment, comprising:
 a processor; and   a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, the operations comprising:
 obtaining, from radio access network equipment, machine learning model training data; 
 obtaining, from machine learning model design platform equipment, a published machine learning model; 
 using the machine learning model training data to train the published machine learning model, resulting in a trained machine learning model; 
 deploying the trained machine learning model for use by the radio access network equipment; 
 receiving performance feedback data representative of operation of the trained machine learning model at the radio access network equipment; and 
 using the performance feedback data to evaluate a performance of the trained machine learning model. 
   
     
     
         14 . The network automation platform equipment of  claim 13 , wherein the operations further comprise identifying an update for the trained machine learning model based on the performance feedback data. 
     
     
         15 . The network automation platform equipment of  claim 14 , wherein the operations further comprise uploading the update for the trained machine learning model to the machine learning model design platform equipment. 
     
     
         16 . The network automation platform equipment of  claim 13 , wherein obtaining the machine learning model training data comprises using an A1 interface to retrieve the model training data. 
     
     
         17 . The network automation platform equipment of  claim 13 , wherein obtaining the machine learning model training data comprises determining the machine learning model training data for the published machine learning model and requesting the machine learning model training data from the radio access network equipment. 
     
     
         18 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
 obtaining, by a radio access network controller, a trained machine learning model from network automation platform equipment, wherein the trained machine learning model was trained using machine learning model training data obtained via a radio access network comprising the radio access network controller;   executing, by the radio access network controller, the trained machine learning model to analyze radio access network data communicated via the radio access network and to make a radio access network control decision for network equipment of the radio access network based on the radio access network data; and   providing, by the radio access network controller, performance feedback data to the network automation equipment, wherein the performance feedback data is related to the executing of the trained machine learning model and wherein the performance feedback data is usable by the network automation equipment to identify an update for the trained machine learning model.   
     
     
         19 . The non-transitory machine-readable medium of  claim 18 , wherein the operations further comprise obtaining, by the radio access network controller, an updated machine learning model, and wherein the updated machine learning model incorporates the update identified from the performance feedback data. 
     
     
         20 . The non-transitory machine-readable medium of  claim 18 , wherein the operations further comprise providing, by the radio access network controller, machine learning model training data to network automation platform equipment, and wherein the machine learning model training data is usable by the network automation equipment to train the trained machine learning model.