Automated machine learning pre-trained model selector

Abstract

Features related to systems and methods for automated generation of a machine learning model based in part on a pretrained model are described. The pretrained model is used as a starting point to augment and retrain according to client specifications. The identification of an appropriate pretrained model is based on the client specifications such as model inputs, model outputs, and similarities between the data used to train the models.

1 . (canceled) 
     
     
         2 . A computer-implemented method comprising:
 under control of one or more processors,
 receiving, from an electronic communication device, a request for a machine learning model, wherein the request identifies:
 (i) an input type to be received by the machine learning model, 
 (ii) an output type to be provided by the machine learning model, and 
 (iii) training data for the machine learning model; 
 
 identifying, from a library of machine learning models, a trained machine learning model, wherein identifying the trained machine learning model is based at least partly on at least one of the input type or the output type identified in the request; and 
 generating the machine learning model using the trained machine learning model and the training data, wherein generating the machine learning model comprises:
 modifying a layer of the trained machine learning model to provide an output of the output type identified in the request or receive the input type identified in the request to form the machine learning model, wherein modifying the layer of the trained machine learning model comprises at least one of adding new nodes to the layer of the trained machine learning model, removing nodes from the layer of the trained machine learning model, or adjusting a hyperparameter for the layer of the trained machine learning model, and 
 training the machine learning model using the training data. 
 
   
     
     
         3 . The computer-implemented method of  claim 2 , further comprising:
 identifying a shape of an input of the input type for the machine learning model, wherein the shape indicates at least one of a number of input values or a data type for an input value to the machine learning model; and   determining that the shape corresponds to a trained model input shape for the trained machine learning model.   
     
     
         4 . The computer-implemented method of  claim 2 , further comprising:
 identifying a portion of the machine learning model based at least in part on annotation information for the machine learning model, wherein the portion includes a layer of the machine learning model; and   determining an adjustment to a weight for a path from a node included in the layer of the machine learning model, wherein the machine learning model including the weight with the adjustment provides the output with an output value of a higher accuracy than the trained machine learning model including the weight without the adjustment.   
     
     
         5 . The computer-implemented method of  claim 2 , further comprising:
 receiving, from the electronic communication device, an image for processing by the machine learning model;   retrieving the machine learning model;   processing the image using the machine learning model to generate an image processing result, the image processing result including at least one of segmentation information or classification information for an object shown in the image; and   transmitting the image processing result to the electronic communication device.   
     
     
         6 . A system comprising:
 one or more computing devices having a processor and a memory, wherein the one or more computing devices execute computer-readable instructions to at least:   receive, from an electronic communication device, a request for a machine learning model, wherein the request identifies:
 (i) an input type to be received by the machine learning model, 
 (ii) an output type to be provided by the machine learning model, and 
 (iii) training data for the machine learning model; 
   identify a first trained machine learning model based at least partly on the request; and   generate the machine learning model using the first trained machine learning model, wherein to generate the machine learning model the one or more computing devices execute computer-readable instructions to at least:
 modify a layer of the first trained machine learning model to provide an output of the output type identified in the request or receive the input type identified in the request to form the machine learning model, wherein modifying the layer of the first trained machine learning model comprises at least one of adding new nodes to the layer of the first trained machine learning model, removing nodes from the layer of the first trained machine learning model, or adjusting a hyperparameter for the layer of the first trained machine learning model, and 
 train at least a portion of the machine learning model using the training data. 
   
     
     
         7 . The system of  claim 6 , wherein the one or more computing devices execute computer-readable instructions to at least:
 identify a shape of the output of the output type for the machine learning model, wherein the shape indicates at least one of a number of output values or a data type for an output value to the machine learning model; and   determine that the shape corresponds to a trained model output shape for the first trained machine learning model.   
     
     
         8 . The system of  claim 6 , wherein to modify the first trained machine learning model, the one or more computing devices execute computer-readable instructions to at least:
 add a node to the layer of the first trained machine learning model.   
     
     
         9 . The system of  claim 6 , wherein to identify the first trained machine learning model, the one or more computing devices execute computer-readable instructions to at least:
 identify the first trained machine learning model and a second trained model based at least in part on the request;   generate a first accuracy metric for the first trained machine learning model based at least partly on processing of a portion of the training data with the first trained machine learning model;   generate a second accuracy metric for the second trained model based at least partly on processing of the portion of the training data with the second trained model; and   determine that the first accuracy metric indicates a higher level of accuracy than the second accuracy metric.   
     
     
         10 . The system of  claim 6 , wherein the one or more computing devices execute computer-readable instructions to at least:
 identify the portion of the machine learning model based at least in part on annotation information for the machine learning model, wherein the portion includes a layer of the machine learning model; and   determine an adjustment to a weight for a path from a node included in the layer of the machine learning model, wherein the machine learning model including the weight with the adjustment generates an output value of a higher accuracy than the machine learning model including the weight without the adjustment.   
     
     
         11 . The system of  claim 6 , wherein the first trained machine learning model is associated with model metadata describing the first trained machine learning model, and
 wherein the one or more computing devices execute computer-readable instructions to at least identify the first trained machine learning model based at least in part on a comparison of the model metadata and metadata included in the request.   
     
     
         12 . The system of  claim 6 , wherein the one or more computing devices execute computer-readable instructions to at least:
 receive, from another computing device, audio data for processing by the machine learning model;   process the audio data using the machine learning model to generate a language processing result, the language processing result including at least one of:
 (i) a transcription of an utterance encoded by the audio data, or (ii) an intent for the utterance encoded by the audio data; and 
 transmit the language processing result to the other computer device. 
   
     
     
         13 . The system of  claim 6 , wherein the one or more computing devices execute computer-readable instructions to at least:
 determine that an accuracy of an output provided by the machine learning model corresponds to a target accuracy; and   activate a network address to receive an input for processing via the machine learning model.   
     
     
         14 . A computer-implemented method comprising:
 under control of one or more processors,
 receiving, from an electronic communication device, a request for a machine learning model, wherein the request identifies:
 (i) an input type, the input type indicating a data format to be received by the machine learning model, 
 (ii) an output type, the output type indicating a type of processing result to be provided by the machine learning model based on the input type, and 
 (iii) training data for the machine learning model; 
 
 identifying, from a library of machine learning models, a first trained machine learning model, wherein identifying the first trained machine learning model is based at least partly on at least one of the input type or the output type identified in the request; and 
 generating the machine learning model using the first trained machine learning model and the training data, wherein generating the machine learning model comprises:
 modifying a layer of the first trained machine learning model to form the machine learning model that provides a processing result of the output type identified in the request or receives the data format indicated by the input type identified in the request, and 
 training at least a portion of the machine learning model using the training data. 
 
   
     
     
         15 . The computer-implemented method of  claim 14 , further comprising:
 identifying a shape of the processing result of the output type for the machine learning model, wherein the shape indicates at least one of a number of output values or a data type for an output value to the machine learning model; and   determining that the shape corresponds to a trained model output shape for the first trained machine learning model.   
     
     
         16 . The computer-implemented method of  claim 14 , wherein modifying the layer of the first trained machine learning model comprises updating a hyperparameter for the layer of the first trained machine learning model. 
     
     
         17 . The computer-implemented method of  claim 14 , wherein identifying the first trained machine learning model comprises:
 identifying the first trained machine learning model and a second trained machine learning model based at least in part on the request;   generating a first accuracy metric for the first trained machine learning model based at least partly on processing of a portion of the training data with the first trained machine learning model;   generating a second accuracy metric for the second trained machine learning model based at least partly on processing of the portion of the training data with the second trained machine learning model; and   determining that the first accuracy metric indicates a higher level of accuracy than the second accuracy metric.   
     
     
         18 . The computer-implemented method of  claim 14 , further comprising:
 identifying the portion of the machine learning model based at least in part on annotation information for the machine learning model, wherein the portion includes a layer of the machine learning model; and   determining an adjustment to a weight for a path from a node included in the layer of the machine learning model, wherein the machine learning model including the weight with the adjustment generates an output value of a higher accuracy than the machine learning model including the weight without the adjustment.   
     
     
         19 . The computer-implemented method of  claim 14 ,
 wherein the first trained machine learning model is associated with model metadata describing the first trained machine learning model, and   wherein identifying the first trained machine learning model is based at least in part on a comparison of the model metadata and metadata included in the request.   
     
     
         20 . The computer-implemented method of  claim 14 , further comprising:
 receiving, from a client device, tabular data for processing by the machine learning model, the tabular data describing a user;   processing the tabular data using the machine learning model to generate a prediction for the user; and   transmitting the prediction to the client device.   
     
     
         21 . The computer-implemented method of  claim 14 , further comprising:
 identifying a topical domain for a user associated with the request, wherein selecting the first trained machine learning model comprises determining that the topical domain relates to a domain associated with the first trained machine learning model.