Abstract: In an approach for visualizing metrics towards optimizing application performance, a processor identifies an application, running in a user interface, on a cloud platform. A processor calculates information metrics of the application. A processor presents the information metrics on the user interface with the application.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to configure the processor to implement a cognitive service for cognitive model tuning with rich deep learning knowledge. The mechanism performs a first model training operation and records training data set and hyperparameter information for the model in a database. The mechanism performs a model testing operation using a testing data set and records metric values that result from the model testing in the database. For a next model training operation for a given model, the mechanism performs an anomalies check for the given model. The mechanism performs a difference comparison on the training data set, hyperparameter information, and the metric values. The mechanism generates a recommendation of a training data set and hyperparameters for the next model training operation. The mechanism performs the next model training operation.

Abstract: In an approach for visualizing metrics towards optimizing application performance, a processor identifies an application, running in a user interface, on a cloud platform. A processor calculates information metrics of the application. A processor presents the information metrics on the user interface with the application.

Abstract: A method, computer system, and a computer program product for de-identifying at least one machine learning (ML) model trained utilizing a set of sensitive data is provided. The present invention may include receiving a corpus of documents. The present invention may then include creating at least one terms list from the received corpus of documents. The present invention may further include de-identifying the at least one ML model based on the created at least one terms list.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to configure the processor to implement a cognitive service for cognitive model tuning with rich deep learning knowledge. The mechanism performs a first model training operation and records training data set and hyperparameter information for the model in a. database. The mechanism performs a model testing operation using a testing data set and records metric values that result from the model testing in the database. For a next model training operation for a given model, the mechanism performs an anomalies check for the given model. The mechanism performs a difference comparison on the training data set, hyperparameter information, and the metric values. The mechanism generates a recommendation of a training data set and hyperparameters for the next model training operation. The mechanism performs the next model training operation.

Abstract: Evaluation of textual annotation models is provided. In various embodiments, an annotation model is applied to textual training data to derive a plurality of automatic annotations. The plurality of automatic annotations is compared to ground truth annotations of the textual data to determine overlapping tokens between the plurality of automatic annotations and the ground truth annotations. Weights are assigned to the overlapping tokens. Based on the weights of the overlapping tokens, scores are determined for the automatic annotations. The scores indicate the correctness of the automatic annotations relative to the ground truth annotations. Based on the scores of for the automatic annotations, an accuracy of the annotation model is determined.