Abstract: A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

Abstract: Techniques facilitating guiding machine learning models and related components are provided. In one example, a computer-implemented method comprises identifying, by a device operatively coupled to a processor, a set of models, wherein the set of models includes respective model components; determining, by the device, one or more model relations among the respective model components, wherein the one or more model relations respectively comprise a vector of component relations between respective pairwise ones of the model components; and suggesting, by the device, a subset of the set of models based on a mapping of the component relations.

Abstract: A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

Abstract: A method of deploying artificial intelligence (AI) model resources includes storing at least one AI model in a model store memory in a plurality of different versions, each different version having a different level of fidelity. When a request to exercise the AI model is received, a processor determines which version of the AI model to exercise for the received request. The determined AI model version is used to serve the received request by exercising input data accompanying the received request. The result of the exercised AI model version is used to respond to the received request.

Abstract: Techniques facilitating guiding machine learning models and related components are provided. In one example, a computer-implemented method comprises identifying, by a device operatively coupled to a processor, a set of models, wherein the set of models includes respective model components; determining, by the device, one or more model relations among the respective model components, wherein the one or more model relations respectively comprise a vector of component relations between respective pairwise ones of the model components; and suggesting, by the device, a subset of the set of models based on a mapping of the component relations.

Abstract: Automated or autonomic techniques for managing deployment of one or more resources in a computing environment based on varying workload levels. The automated techniques may comprise predicting a future workload level based on data associated with the computing environment. Then, an estimation is performed to determine whether a current resource deployment is insufficient, sufficient, or overly sufficient to satisfy the future workload level. Then, one or more actions are caused to be taken when the current resource deployment is estimated to be insufficient or overly sufficient to satisfy the future workload level. Actions may comprise resource provisioning, resource tuning and/or admission control.

Abstract: A method for executing on a first computer an application having an installed image prepared on a second computer, wherein the installed image is virtually installed on the first computer, is disclosed. The method includes emulating on the first computer a native environment of the second computer. The method further includes detecting an operation of the application upon data, wherein the operation requires an operation on data located on the first computer, and wherein a copy of the data is located in the virtually installed image. The method further includes directing the operation of the application to operate on the data located in the virtually installed image.