Abstract: Systems, methods, and computer-readable media are disclosed for validating a machine learning model. In one aspect, a machine learning model validation system can receive a test machine learning model, analyze an output of the test machine learning model, determine a degree of similarity between the test machine learning model and one or more machine learning models stored in a database based on the output of the test machine learning model, and determining whether the test machine learning model complies with a set of validation rules based on the degree of the similarity with respect to one or more thresholds.

Abstract: Systems, methods, and computer-readable media are disclosed for a dynamic and intelligent machine learning scheduling platform for running multiple machine learning models simultaneously. The present technology includes receiving output data of a first machine learning model running on an edge device. Further, the present technology includes accessing a set of dynamic rules for scheduling a second machine learning model to run on the edge device. As follows, the present technology includes determining to run the second machine learning model on the edge device in accordance with the set of rules where the first machine learning model and the second machine learning model are run on the edge device in parallel.

Abstract: Systems, methods, and computer-readable media are disclosed for dynamically adjusting a configuration of a pre-processor and/or a post-processor of a machine learning system. In one aspect, a machine learning system can receive raw data at a pre-processor where the pre-processor being configured to generate pre-processed data, train a machine learning model based on the pre-processed data to generate output data, process the output data at a post-processor to generate inference data, and adjust, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data.

Abstract: Systems, methods, and computer-readable media are disclosed for validating a machine learning model. In one aspect, a machine learning model validation system can receive a test machine learning model, analyze an output of the test machine learning model, determine a degree of similarity between the test machine learning model and one or more machine learning models stored in a database based on the output of the test machine learning model, and determining whether the test machine learning model complies with a set of validation rules based on the degree of the similarity with respect to one or more thresholds.

Abstract: The present disclosure is directed to system and methods for providing machine learning tools such as Kubeflow and other similar ML platforms with human-in-the-loop capabilities for optimizing the resulting machine models. In one aspect, a machine learning integration tool includes memory having computer-readable instructions stored therein and one or more processors configured to execute the computer-readable instructions to execute a workflow associated with a machine learning process; determine, during execution of the machine learning process, that non-automated feedback is required; generate a virtual input unit for receiving the non-automated feedback; modify raw data used for the machine learning process with the non-automated feedback to yield updated data; and complete the machine learning process using the updated data.