Systems, methods, and storage media for identifying an object in a photograph using a machine learning system

A method of identifying an object in a photograph, the method comprising:

• sending an initial request to identify the object in the photograph from a user device to an application programming interface (API);
• generating, at the API, a pre-signed Uniform Resource Locator (URL) associated with a storage system;
• generating metadata associated with the initial request;
• sending an initial response to the initial request;
• uploading the photograph from a user device directly to a location associated with the pre-signed URL;
• sending a second request to identify the object in the photograph from the user device to the API;
• providing a second response to the second request;
• obtaining the photograph from the storage system;
• using the photograph to invoke a first trained model from a machine learning system endpoint;
• using the photograph to invoke a second trained model from the machine learning system endpoint;
• obtaining from the second trained model a list of one or more most likely identification probabilities associated with the object;
• updating the metadata associated with the object at the API;
• sending a third request from the user device to the API, wherein the third request comprises a request to receive information associated with the list of one or more most likely identification probabilities;
• and
• displaying at the user device one or more of the list of one or more most likely identification probabilities associated with the object, and information associated with the one or more of the list of one or more most likely identification probabilities associated with the object.

A non-transient computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method for operating a machine-learning system, the method comprising:

• preparing a plurality of data files for training by associating a label with each of the plurality of data files, identifying and fixing errors in the plurality of data files, and uploading the plurality of data files to a storage device;
• obtaining a versioning snapshot of the plurality of data files uploaded to a storage device;
• providing metadata associated with the plurality of data files to the machine-learning system;
• splitting the plurality of data files into a plurality of data sets, including at least a training data set, a testing data set, and a validation data set;
• creating record IO data files comprising a plurality of training set record IO files, a plurality of testing set record IO files, a plurality of validation set record IO files, and a plurality of full set record IO files;
• merging the plurality of record IO files for each data set of the plurality of data sets;
• passing the merged record IO files to the storage device;
• identifying hyperparameter settings for use in association with training the merged full set record IO files by (1) selecting a portion of the merged testing IO files and a portion of the merged validation IO files, (2) applying a plurality of hyperparameters to the selected portion of the merged testing IO files and the selected portion of the merged validation IO files across a plurality of training jobs, and (3) reviewing one or more metrics related to the plurality of training jobs;
• storing artifacts related to the plurality of training jobs at the storage device;
• using the identified hyperparameter settings and the artifacts to train the merged full set record IO files;
• storing one or more metrics and artifacts associated with training the merged full set record IO files;
• using the one or more metrics and artifacts associated with training the merged full set record IO files to create a final machine learning model;
• obtaining the final machine learning model from a machine learning system registry;
• and
• deploying the final machine learning model to one or more machine learning system endpoints.

A non-transient computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method for identifying an object in a photograph, the method comprising:

• sending an initial request to identify the object in the photograph from a user device to an application programming interface (API);
• generating, at the API, a pre-signed uniform resource locator (URL) associated with a storage system;
• generating metadata associated with the initial request;
• sending an initial response to the initial request;
• uploading the photograph from a user device directly to a location associated with the pre-signed URL;
• sending a second request to identify the object in the photograph from the user device to the API;
• providing a second response to the second request;
• obtaining the photograph from the storage system;
• using the photograph to invoke a first trained model from a machine learning system endpoint;
• using the photograph to invoke a second trained model from the machine learning system endpoint;
• obtaining from the second trained model a list of one or more most likely identification probabilities associated with the object;
• updating the metadata associated with the object at the API;
• sending a third request from the user device to the API, wherein the third request comprises a request to receive information associated with the list of one or more most likely identification probabilities;
• and
• displaying at the user device one or more of the list of one or more most likely identification probabilities associated with the object, and information associated with the one or more of the list of one or more most likely identification probabilities associated with the object.