Amazon Technologies, Inc.

Laurence Louis Eric Rouesnel

Stefano Stefani

Vladimir Zhukov

Leo Parker Dirac

Andrea Olgiati

Zohar Karnin

Tanya Bansal

Orchid Majumder

Fan Li

Piali Das

Ravikumar Anantakrishnan Venkateswar

Patricia Grao Gil

Philip Gautier

Maximiliano Maccanti

Gowda Dayananda Anjaneyapura Range

Lakshmi Naarayanan Ramakrishnan

Arun Babu Nagarajan

Urvashi Chowdhary

Rahul Iyer

Hyunsu Cho

Techniques for adaptive machine learning training via in-flight feature modification are described. A training monitor captures training data during the training of a machine learning model, and a metric generator creates metrics such as feature importance metrics based on the data. A rule evaluation engine determines whether any modification conditions are met for any of the features based on the metrics, and based on such a determination can cause the in-flight training job to be modified.

Adaptive machine learning training via in-flight feature modification

Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

Automated machine learning pipeline exploration and deployment

Techniques for feature engineering pipeline generation for machine learning using decoupled dataset analysis and interpretation are described. A feature engineering engine obtains a dataset and utilizes a number of analyzers to generate data facts associated with the columnar values of the dataset. The data facts are consolidated together as a set of data statements that are used by multiple interpretation engines that implement different strategies for treating the data in order to generate feature engineering pipeline code.

Feature engineering pipeline generation for machine learning using decoupled dataset analysis and interpretation

Methods, systems, and computer-readable media for automated problem detection for machine learning models are disclosed. A machine learning analysis system receives data associated with use of a machine learning model. The data was collected by a machine learning inference system and comprises input to the model or a plurality of inferences representing output of the machine learning model. The machine learning analysis system performs analysis of the data associated with the use of the machine learning model. The machine learning analysis system detects one or more problems associated with the use of the machine learning model based at least in part on the analysis. The machine learning analysis system initiates one or more remedial actions associated with the one or more problems associated with the use of the machine learning model.

Automated problem detection for machine learning models

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New York, NY, United States of America

Laurence Louis Eric Rouesnel