Abstract: A system for reducing computational load for training machine learning models is provided. The system may provide an end-to-end-solution for automating development, testing and updating of machine learning models in various operational environments. The system may determine which machine learning models included in a computer program product need to be retrained in response to a change in training data. For a computer program product that includes multiple models, the system only retrains target models, resulting in significant savings in computing resources. The system may also reduce the number of machine learning models that need to be generated for testing environments, further reducing consumption of computational resources.

Abstract: An artificial intelligence (“AI”) system for tuning a machine learning interactive voice response system is provided. The AI system may perform analysis of outputs generated by the machine learning models. The AI system may determine an expected model output for a given test input. The AI system may determine accuracy, precision and recall scores for an actual output garneted in response to the test input. The system may determine performance metrics for interim outputs generated by individual machine learning models within the interactive voice response system. The AI system may replace malfunctioning models with replacement models.

Abstract: A system for reducing computational load for training machine learning models is provided. The system may provide an end-to-end-solution for automating development, testing and updating of machine learning models in various operational environments. The system may determine which machine learning models included in a computer program product need to be retrained in response to a change in training data. For a computer program product that includes multiple models, the system only retrains target models, resulting in significant savings in computing resources. The system may also reduce the number of machine learning models that need to be generated for testing environments, further reducing consumption of computational resources.

Abstract: A system for horizontal scaling of retraining machine learning models across operational domains is provided. The system may reduce computational overhead associated model retraining. The system may include an artificial intelligence (“AI”) engine that determines target machine learning models that need to be retrained in response to changed training data. The AI engine may assign daemons to the target models. The daemons may gather retraining requirements such as source code and training data. The daemons may schedule the target models for retraining on a CPU or a GPU based model training system.

Abstract: An artificial intelligence (“AI”) system for tuning a machine learning interactive voice response system is provided. The AI system may perform analysis of outputs generated by the machine learning models. The AI system may determine an expected model output for a given test input. The AI system may determine accuracy, precision and recall scores for an actual output garneted in response to the test input. The system may determine performance metrics for interim outputs generated by individual machine learning models within the interactive voice response system. The AI system may replace malfunctioning models with replacement models.

Abstract: Methods for analyzing a query is provided. Methods may include receiving a query from a user. Methods may include dissecting the query into a plurality of word segments. Methods may include determining, based on a predetermined ontology, which of the plurality of words satisfies a predetermined ontological threshold and which words do not satisfy the predetermined ontological threshold. Methods may include discarding the words that do not satisfy the predetermined ontological threshold. Methods may include determining a part of speech associated with each word based on the content of the query. Methods may include determining a concept associated with each word based on the content of the query. Methods may include displaying to the user word that satisfies the predetermined threshold alongside the associated part of speech and associated concept. Methods may include enabling the user to change each word, each concept and each part of speech.

Abstract: Methods for analyzing a query is provided. Methods may include receiving a query from a user. Methods may include dissecting the query into a plurality of word segments. Methods may include determining, based on a predetermined ontology, which of the plurality of words satisfies a predetermined ontological threshold and which words do not satisfy the predetermined ontological threshold. Methods may include discarding the words that do not satisfy the predetermined ontological threshold. Methods may include determining a part of speech associated with each word based on the content of the query. Methods may include determining a concept associated with each word based on the content of the query. Methods may include displaying to the user word that satisfies the predetermined threshold alongside the associated part of speech and associated concept. Methods may include enabling the user to change each word, each concept and each part of speech.