Abstract: A method and system for managing a dataset. An artificial intelligence (AI) model is to be used on the dataset. A data mask describes a labeling status of the data items. A loop is repeated until patience parameters are satisfied. The loop comprises receiving trusted labels provided by trusted labelers; updating the data mask; from a labelled data items subset, training the AI model; cloning the trained AI model into a local AI model on processing nodes; creating and chunking a randomized unlabeled subset into data subsets for dispatching to the processing nodes; receiving an indication that predicted label answers have been inferred by the processing nodes using the local AI model; computing a model uncertainty measurement from statistical analysis of the predicted label answers. The patience parameters include one or more of a threshold value on the model uncertainty measurement and information gain between different training cycles.

Abstract: Persistent storage contains a training dataset and a test dataset, each with units of text labelled from a plurality of categories. A machine learning model has been trained with the training dataset to classify input units of text into the plurality of categories. One or more processors are configured to: read the training dataset or the test dataset; determine distributional properties of the training dataset or the test dataset; determine, using the machine learning model, saliency maps for tokens in the training dataset or the test dataset; perturb, by way of token insertion, token deletion, or token replacement, the training dataset or the test dataset into an expanded dataset; obtain, using the machine learning model, classifications into the plurality of categories for the expanded dataset; and based on the distributional properties, the saliency maps, and the classifications, identify causes of failure for the machine learning model.

Abstract: A method and server for optimizing hyperparameter tuples for training production-grade artificial intelligence (AI) models. For each one of the AI models, AI model features are extracted and, for the one AI model, an initial distribution of n hyperparameter tuplesis created considering the extracted AI model features therefor. A loop is repeated, until metric parameters are satisfied, comprising: evaluating latency from training the one AI model for each of the n hyperparameters tuples; evaluating model uncertainty from training the one AI model for each of the n hyperparameters tuples; for each of the n hyperparameters tuples, computing a blended quality measurement from the evaluated latency and evaluated model uncertainty; replacing m hyperparameter tuples having the worst blended quality measurements with m newly generated hyperparameter tuples. The metric parameters include one or more of a threshold value on model uncertainty and blended quality measurement gain between successive loops.

Abstract: A method and a server for updating a dynamic list of labeling tasks. One or more labels are received, each label associated to one labeling task; the one or more received labels are inserted into a dataset; an artificial intelligence (AI) model is trained on labeled data items from the dataset; predicted labels are obtained for a plurality of unlabeled data items from the dataset by applying the model thereon; a model-uncertainty measurement is computed by applying one or more regularization methods; relevancy values are computed for at least a subset of the predicted labels taking into account the predicted label and the model-uncertainty measurement; the data items corresponding to the labeling tasks with the highest relevancy values are inserted in the dynamic list; and the dynamic list is reordered upon computing of the relevancy values.

Abstract: A method and system for managing a dataset. An artificial intelligence (AI) model is to be used on the dataset. A data mask describes a labeling status of the data items. A loop is repeated until patience parameters are satisfied. The loop comprises receiving trusted labels provided by trusted labelers; updating the data mask; from a labelled data items subset, training the AI model; cloning the trained AI model into a local AI model on processing nodes; creating and chunking a randomized unlabeled subset into data subsets for dispatching to the processing nodes; receiving an indication that predicted label answers have been inferred by the processing nodes using the local AI model; computing a model uncertainty measurement from statistical analysis of the predicted label answers. The patience parameters include one or more of a threshold value on the model uncertainty measurement and information gain between different training cycles.

Abstract: A method and server for optimizing hyperparameter tuples for training production-grade artificial intelligence (AI) models. For each one of the AI models, AI model features are extracted and, for the one AI model, an initial distribution of n hyperparameter tuplesis created considering the extracted AI model features therefor. A loop is repeated, until metric parameters are satisfied, comprising: evaluating latency from training the one AI model for each of the n hyperparameters tuples; evaluating model uncertainty from training the one AI model for each of the n hyperparameters tuples; for each of the n hyperparameters tuples, computing a blended quality measurement from the evaluated latency and evaluated model uncertainty; replacing m hyperparameter tuples having the worst blended quality measurements with m newly generated hyperparameter tuples. The metric parameters include one or more of a threshold value on model uncertainty and blended quality measurement gain between successive loops.