How Uber Uses AutoML
4 engineering articles about AutoML from Uber's engineering team
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The article discusses Uber's evolution in machine learning (ML) through its centralized platform, Michelangelo, highlighting its transition from predictive to generative AI.
ApacheApache SparkAutoMLDeep LearningDockerGenerative AIHugging FaceKerasKubernetesPaLMPrompt EngineeringPyTorchTensorFlowXGBoost
Kai Wang, Min Cai, Joseph Wang, Eric Chen
28 min read
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The article discusses the integration of Elastic Horovod with Ray, focusing on how this combination enhances distributed deep learning training by enabling autoscaling and fault tolerance.
ApacheApache SparkAutoMLAWSAzureDaskDeep LearningKubernetesMachine LearningModinPandasPyTorchXGBoost
Travis Addair, Xu Ning, Richard Liaw
15 min read
Includes Code
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Ludwig version 0. 3 introduces significant enhancements, including hyperparameter optimization, support for Transformers, and integration with TensorFlow 2.
Kerri Brown, Piero Molino, Yaroslav Dudin
10 min read
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The article introduces Michelangelo, Uber's internal machine learning platform designed to democratize machine learning and streamline the process of building, deploying, and operating ML solutions...
Jeremy Hermann, Mike Del Balso
24 min read
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