Amazon has created an open source toolkit for automated machine learning, called AutoGluon, designed to make it easier for software developers to take advantage of deep learning models in their applications. AutoGluon is intended for both machine learning experts and beginners, the company says.
Officially launched January 9, AutoGluon lets developers harness machine learning models with image, text, or tabular data sets, sans any need to manually experiment. Developers can achieve strong, predictive performance in their applications.
Accessible from the project website or GitHub, AutoGluon automates many decisions for developers, enabling them to produce a high-performance neural networking model with as few as three lines of code. AutoGluon leverages available compute resources to find the strongest model within its allotted runtime. Python 3.6 or Python 3.7 is required; AutoGluon currenty is limited to Linux, although MacOS and Windows support is planned.
AutoGluon capabilities allow users to:
- Prototype deep learning solutions for a data set in few lines of code.
- Leverage hyperparameter tuning, model selection and architecture search, and data processing.
- Improve existing neural network models and data pipelines.
- Take advantage of APIs to automatically improve predictive performance in applications without expert knowledge.
In explaining the reasoning behind AutoGluon, Amazon said deployment of deep learning models with state-of-the-art inferencing accuracy typically has required extensive expertise. Developers have had to invest a considerable amount of time and effort into training deep learning models. Despite advancements such as the Keras library, for more easily specifying parameters and layers in deep learning models, developers have had to grapple with complex issues such as data pre-processing and hyperparameter tuning. AutoGluon is intended to democratize machine learning and make deep learning available to all developers.
This story, “Amazon’s AutoGluon automates deep learning for devs” was originally published by
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