Amazon Technologies, Inc.

Anushri Mainthia

Rahul Bhotika

Avinash Aghoram Ravichandran

Gurumurthy Swaminathan

Ranju Das

Barath Balasubramanian

Rakesh Madhavan Nambiar

Prakash Krishnan

Joaquin Zepeda Salvatierra

Niels Brouwers

Anant Patel

Shaun Ryan James McDowell

Shaun Ryan James Mcdowell

Techniques for feedback-based training may include selecting a scoring machine learning model based at least in part on a test metric, and applying the model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, a prediction and an importance ranking score for the prediction. Techniques for feedback-based training may further include selecting, based on the importance ranking scores, a result of the application of the model on the unlabeled dataset, providing the result and requesting feedback on the result via a graphical user interface, receiving the feedback via the graphical user interface, adding data from the unlabeled dataset into a training dataset when the feedback indicates a verified result, and retraining the model using the training dataset with the data added from the unlabeled dataset to generate a retrained model.

Feedback-based training for anomaly detection

Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.

Anomaly detection using feedback training

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Seattle, WA, United States of America

Anushri Mainthia