Abstract: A method for product release estimates a ROI of a new product feature of a product. The method identifies a product component which undergoes change with weightage responsive to epic details and non-functional requirements. The method classifies a component action to provide an action classification. The method calculates an effort to create the new feature responsive to the action and a complexity classification determined from at least the weightage. The method calculates an interval of effort to produce the new product feature responsive to the effort and similar epic matches relating to the new product feature. The method calculates an estimated public release time of the new feature responsive to the similar epic matches relating to the new feature. The method generates and follows a schedule responsive to the effort, the interval of effort, the estimated public release time, and the ROI to make the product having the new feature.

Abstract: A computer implemented system, method, and computer program product are disclosed for managing a machine learning model operation (MLOps) for cost forecasting models. The MLOps receives a request for a subscription machine learning (ML) model by a tenant having a corresponding tenant configuration profile and automatically selects a subscription ML model in a model registry based on the tenant configuration profile. The MLOps deploys the selected subscription ML model to the tenant and monitors usage by the tenant of the currently operating ML model at a pre-determined refresh frequency. The MLOps determines whether the currently operating ML model exceeds a pre-determined accuracy threshold, and automatically deploys a second subscription ML model from the model registry to the tenant in place of the currently operating ML model in response to the pre-determined accuracy threshold being exceeded.

Abstract: Disclosed embodiments provide a computer-implemented method for cloud computing infrastructure cost forecasting. Resource profiles are computed for one or more cloud resources. A scheduled pattern detection process is performed for each of the one or more cloud resources to check for periodic behaviors. A similar consumer detection process is performed for each of the one or more cloud resources to identify other entities that have a similar cloud computing resource usage pattern, which can serve as supervised learning data for neural networks of disclosed embodiments. Data is input to a neural network, where the input data includes the resource profile, an operational maturity score, and/or one or more similar consumer patterns, in order to obtain a cost forecast from the neural network, based on the input data.

Abstract: A computer-implemented method, according to one embodiment, includes collecting user data associated with a user's behavior with respect to a predetermined product and analyzing the user data for determining a subset of the user data that constitutes an implicit request for a new candidate feature for the predetermined product. A new candidate feature for the predetermined product that satisfies the implicit request is determined based on the subset of the user data. The method further includes outputting an indication of the determined new candidate feature to a device associated with development of the predetermined product. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform the foregoing method.