Abstract: Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model.

Abstract: An approach is provided that receives multimedia content and extracts a set of metadata from the content. The extraction of metadata includes performing image analysis on the multimedia content. The approach then analyzes the set of metadata with the analysis resulting in a set of regulations that apply to the multimedia content. The approach compares the set of metadata to the set of regulations and allows publication of the multimedia content when the comparison reveals that the multimedia content is in compliance with the set of regulations, and inhibits publication of the multimedia content when the multimedia content fails to comply with the set of regulations.

Abstract: An approach is provided that receives multimedia content and extracts a set of metadata from the content. The extraction of metadata includes performing image analysis on the multimedia content. The approach then analyzes the set of metadata with the analysis resulting in a set of regulations that apply to the multimedia content. The approach compares the set of metadata to the set of regulations and allows publication of the multimedia content when the comparison reveals that the multimedia content is in compliance with the set of regulations, and inhibits publication of the multimedia content when the multimedia content fails to comply with the set of regulations.

Abstract: Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model.

Abstract: Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model.

Abstract: Embodiments relate to a system, program product, and method for evaluating and controlling configuration of a build manifest. An application build manifest is discovered and is subjected to parsing process in which one or more components that comprise the application are identified. The build manifest is monitored for changes to the identified components, and a change notification is generated in response to a change in an identified component. Each generated change notification is assigned a classification. The change notifications are applied selectively to update the manifest, wherein the selective update is based on the classification of the change notification.

Abstract: Embodiments relate to a system, program product, and method for evaluating and controlling configuration of a build manifest. An application build manifest is discovered and is subjected to parsing process in which one or more components that comprise the application are identified. The build manifest is monitored for changes to the identified components, and a change notification is generated in response to a change in an identified component. Each generated change notification is assigned a classification. The change notifications are applied selectively to update the manifest, wherein the selective update is based on the classification of the change notification.

Abstract: A method and system of optimizing parameters of a microservice-based application is provided. A microservice infrastructure of the microservice-based application is determined. One or more optimization objectives related to the microservice-based application are determined. Different combinations of timeout and retry values are tested for each microservice. A reward value is calculated for each of the different combinations of timeout and retry values. The microservice infrastructure is set to a combination of timeout and retry values having a highest reward value for the one or more optimization objectives.

Abstract: A method and system of optimizing parameters of a microservice-based application is provided. A microservice infrastructure of the microservice-based application is determined. One or more optimization objectives related to the microservice-based application are determined. Different combinations of timeout and retry values are tested for each microservice. A reward value is calculated for each of the different combinations of timeout and retry values. The microservice infrastructure is set to a combination of timeout and retry values having a highest reward value for the one or more optimization objectives.

Abstract: Techniques for estimating runtimes of one or more machine learning tasks are provided. For example, one or more embodiments described herein can regard a system that can comprise a memory that stores computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can extract a parameter from a machine learning task. The parameter can define a performance characteristic of the machine learning task. Also, the computer executable components can comprise a model component that can generate a model based on the parameter. Further, the computer executable components can comprise an estimation component that can generate an estimated runtime of the machine learning task based on the model.