Abstract: An artificial intelligence (AI) platform to support selective replacement of one or more image layers of a container image build. A metadata file is subject to natural language processing and one or more corresponding vector representations are created and subject to evaluation by a set of artificial neural networks (ANNs). A first ANN assesses each vector representation with respect to compliance and operability, and the second ANN selectively assesses the vector representation(s) with respect to similarity with one or more compliant vector representations. In response to the assignment of the second score, at least one vector representation of the received metadata file is selectively replaced with at least one compliant vector representation. The metadata file is selectively provisioned with the selectively replaced vector representation(s).

Abstract: Embodiments of the present invention provide an approach for optimizing usage and providing recommendations to users in a hybrid cloud environment. Specifically, user configuration data and deadline for job execution for a job to be executed is collected. A broker queries available queues to determine a wait time corresponding to each of the available queues for the job based on the user configuration data. The wait times are compared to the deadline for job execution. If the deadline cannot be met, a machine learning module suggests modifications to the user configuration to reduce wait times and meet the deadline.

Abstract: A computer-implemented method of determining installation compatibility includes identifying one or more entities of an uninstalled operator. The identified one or more entities of the uninstalled operator are parsed and information is extracted from the one or more entities. An existing operator installed on a target container cluster is parsed and information extracted from the entities of the existing operator. The extracted information from the uninstalled operator is compared with the extracted information from the existing operator. A disruption risk to operation of the target container cluster is ranked based on the comparing of the extracted information of the uninstalled operator with the extracted information of the existing operation.

Abstract: An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

Abstract: An artificial intelligence (AI) platform to support selective replacement of one or more image layers of a container image build. A metadata file is subject to natural language processing and one or more corresponding vector representations are created and subject to evaluation by a set of artificial neural networks (ANNs). A first ANN assesses each vector representation with respect to compliance and operability, and the second ANN selectively assesses the vector representation(s) with respect to similarity with one or more compliant vector representations. In response to the assignment of the second score, at least one vector representation of the received metadata file is selectively replaced with at least one compliant vector representation. The metadata file is selectively provisioned with the selectively replaced vector representation(s).

Abstract: Embodiments are provided for intelligent recommendations for program code. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an evaluation component that determines that first program code pertains to a defined category representing a defined cost to execute the first program code by a cloud computing service. The computer-executable components also can include a recommendation component that generates a recommendation for second program code that satisfies a similarity criterion with respect to the first program code. The second program code pertains to a category representing a cost to execute the second program code by the cloud computing service, where the cost is less than the defined cost.

Abstract: An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

Abstract: A method, computer system, and a computer program product for host validation is provided. The present invention may include receiving a job from a user. The present invention may include selecting, by a scheduler, a host in a hybrid cloud environment to run the received job. The present invention may include classifying, by a learning component, the selected host's subsystems. The present invention may include determining, based on the classification, that the selected host can run the received job.

Abstract: Described herein are techniques related to a semiconductor fabrication process that facilitates the enhancement of systemic conformities of patterns of the fabricated semiconductor wafer. A semiconductor wafer with maximized systemic conformities of patterns will maximize the electrical properties and/or functionality of the electronic devices formed as part of the fabricated semiconductor wafer. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Embodiments are provided for intelligent recommendations for program code. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an evaluation component that determines that first program code pertains to a defined category representing a defined cost to execute the first program code by a cloud computing service. The computer-executable components also can include a recommendation component that generates a recommendation for second program code that satisfies a similarity criterion with respect to the first program code. The second program code pertains to a category representing a cost to execute the second program code by the cloud computing service, where the cost is less than the defined cost.

Abstract: Described herein are techniques related to a semiconductor fabrication process that facilitates the enhancement of systemic conformities of patterns of the fabricated semiconductor wafer. A semiconductor wafer with maximized systemic conformities of patterns will maximize the electrical properties and/or functionality of the electronic devices formed as part of the fabricated semiconductor wafer. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: The present invention may include an embodiment that receives a deployment declaration in a natural language. The embodiment may detect one or more sequencing entities and one or more parameter entities using trained natural language processing. The embodiment may sequence a configuration file based on the one or more sequencing entities. The embodiment may determine a plurality of configuration parameters in the sequenced configuration file. The embodiment may substitute a configuration parameter from the plurality of configuration parameters of the sequenced configuration file with the one or more parameter entities. The embodiment may align the plurality of configuration parameters of the sequenced configuration file based on organization compliance data and deploys a tuned cloud service using the sequenced configuration file.

Abstract: The present invention may include an embodiment that receives a deployment declaration in a natural language. The embodiment may detect one or more sequencing entities and one or more parameter entities using trained natural language processing. The embodiment may sequence a configuration file based on the one or more sequencing entities. The embodiment may determine a plurality of configuration parameters in the sequenced configuration file. The embodiment may substitute a configuration parameter from the plurality of configuration parameters of the sequenced configuration file with the one or more parameter entities. The embodiment may align the plurality of configuration parameters of the sequenced configuration file based on organization compliance data and deploys a tuned cloud service using the sequenced configuration file.

Abstract: A scheduler node in a blockchain network may receive data associated with a machine learning model. The scheduler node may measure a drift of the machine learning model for a first aspect of the data. The scheduler node may determine if the drift of the machine learning model is greater than a threshold. The scheduler node may schedule, in response to the drift being greater than the drift threshold, a retraining transaction for the machine learning model.

Abstract: In one embodiment, a method includes obtaining wafer measurements of a characteristic of a semiconductor wafer at each of a plurality of process steps during a semiconductor wafer fabrication process, where each of the wafer measurements is associated with a spatial location on the semiconductor wafer from which the measurement is obtained. The method may further include creating a process step fingerprint from the obtained wafer measurements for each process step. The method may further include correlating the process step fingerprint of one of the plurality of process steps to the process step fingerprint of another one of the plurality of process steps to produce a transfer function.

Abstract: Methods and systems for phonological clustering are disclosed. A method includes: segmenting, by a computing device, a sentence into a plurality of tokens; determining, by the computing device, a plurality of phoneme variants corresponding to the plurality of tokens; clustering, by the computing device, the plurality of phoneme variants; creating, by the computing device, an initial vectorization of the plurality of phoneme variants based on the clustering; embedding, by the computing device, the initial vectorization of the plurality of phoneme variants into a deep learning model; and determining, by the computing device, a radial set of phoneme variants using the deep learning model.

Abstract: In one embodiment, a method includes obtaining wafer measurements of a characteristic of a semiconductor wafer at each of a plurality of process steps during a semiconductor wafer fabrication process, where each of the wafer measurements is associated with a spatial location on the semiconductor wafer from which the measurement is obtained. The method may further include creating a process step fingerprint from the obtained wafer measurements for each process step. The method may further include correlating the process step fingerprint of one of the plurality of process steps to the process step fingerprint of another one of the plurality of process steps to produce a transfer function.

Abstract: Methods and systems for phonological clustering are disclosed. A method includes: segmenting, by a computing device, a sentence into a plurality of tokens; determining, by the computing device, a plurality of phoneme variants corresponding to the plurality of tokens; clustering, by the computing device, the plurality of phoneme variants; creating, by the computing device, an initial vectorization of the plurality of phoneme variants based on the clustering; embedding, by the computing device, the initial vectorization of the plurality of phoneme variants into a deep learning model; and determining, by the computing device, a radial set of phoneme variants using the deep learning model.

Abstract: Described herein are techniques related to a semiconductor fabrication process that facilitates the enhancement of systemic conformities of patterns of the fabricated semiconductor wafer. A semiconductor wafer with maximized systemic conformities of patterns will maximize the electrical properties and/or functionality of the electronic devices formed as part of the fabricated semiconductor wafer. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Described herein are techniques related to a semiconductor fabrication process that facilitates the enhancement of systemic conformities of patterns of the fabricated semiconductor wafer. A semiconductor wafer with maximized systemic conformities of patterns will maximize the electrical properties and/or functionality of the electronic devices formed as part of the fabricated semiconductor wafer. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.