Abstract: An apparatus comprises a processing device configured to receive a request to generate a synthesized document comprising one or more search terms, and to extract, utilizing a first machine learning model, keywords from a set of documents. The processing device is also configured to select first content for inclusion in a first section of the synthesized document based on a similarity of the search terms and the extracted keywords from corresponding first sections of the set of documents, and to determine, utilizing a second machine learning model that takes as input the selected first content, a set of terms for a second section of the synthesized document. The processing device is further configured to select second content for inclusion in the second section of the synthesized document based on a similarity of the determined set of terms and the extracted keywords from corresponding sections of the set of documents.

Abstract: One example method includes acquiring data from a knowledge base that includes message codes indicating conditions that occurred during performance of one or more test runs of a computing operation, and the message codes are included in message code sequences, processing the data by mapping the message codes, and message codes included in a customer issue log, to codes that are readable by a machine learning process, transforming the data to generate an output that comprises, for each message code sequence, relationships between each of the message codes in that message code sequence, extracting features from the transformed data, and the extracting generates multiple datasets that include the features, performing a similarity comparison by comparing a customer field issue with the datasets, and based on the similarity comparison, identifying and recommending a solution to the customer field issue.

Abstract: An information handling system instantiates a hardware configuration generator to generate platform configurations. The hardware configuration generator provides a ranked list of the platform configurations, receives platform customer service request information, provides a ranked list of part numbers associated with the platform configurations based upon the customer service request information, and modifies the ranked list of platform configurations based upon the ranked list of part numbers.

Abstract: One example method includes acquiring data from a knowledge base that includes message codes indicating conditions that occurred during performance of one or more test runs of a computing operation, and the message codes are included in message code sequences, processing the data by mapping the message codes, and message codes included in a customer issue log, to codes that are readable by a machine learning process, transforming the data to generate an output that comprises, for each message code sequence, relationships between each of the message codes in that message code sequence, extracting features from the transformed data, and the extracting generates multiple datasets that include the features, performing a similarity comparison by comparing a customer field issue with the datasets, and based on the similarity comparison, identifying and recommending a solution to the customer field issue.

Abstract: Techniques described herein relate to a method for performing testing operations for information handling systems.

Abstract: A system, method, and computer-readable medium are provided that engages in a data-driven and machine learning-based approach to arrive at high-value, system under test configurations for validation. Embodiments determine all the possible configurations for a computer platform, considering the variety of processors, boards, adapters, and the like, and then utilize a pseudo-ensemble clustering methodology that combines a k-means clustering technique with a neural-network based Kohenon self-organizing map competitive clustering technique to associate like configurations, and then utilizes a data-driven scoring methodology on the clustered configurations to prioritize those configurations to be validation tested.

Abstract: A system, method, and computer-readable medium are provided that engages in a data-driven and machine learning-based approach to arrive at high-value, system under test configurations for validation. Embodiments determine all the possible configurations for a computer platform, considering the variety of processors, boards, adapters, and the like, and then utilize a pseudo-ensemble clustering methodology that combines a k-means clustering technique with a neural-network based Kohenon self-organizing map competitive clustering technique to associate like configurations, and then utilizes a data-driven scoring methodology on the clustered configurations to prioritize those configurations to be validation tested.