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AWS SageMaker Algorithms and Frameworks Get Refresh
Amazon Web Services (AWS) is rolling out several improvements to its SageMaker machine learning modeling platform, the company announced last week.
The improvements apply to the program's built-in BlazingText, DeepAR and Linear Learning algorithms.
For example, AWS said it improved the accuracy and ease of using DeepAR's algorithms for forecasting so that "missing values are now handled within the model." Its DeepAR algorithm also now supports seasonality patterns and other "custom time-varying features," as well as multiple groupings of time series.
BlazingText has been improved, according to the company, with the addition of a Word2Vec algorithm designed to optimize usage of GPU hardware.
Other improvements include "meaningful vectors for out-of-vocabulary (OOV) words that do not appear in the training dataset" and new support for "high-speed multi-class and multi-label text classification," among other improvements.
Users of Linear Learning will now have multi-class classification and other improved ways of sorting and classifying data.
SageMaker also now supports Chainer 4.1, offering pre-configured containers within which users will find Layer-wise Adaptive Rate Scaling (LARS), which AWS says improves the training of networks with "large batch sizes."
The above changes are currently rolling out across AWS instances worldwide.
About the Author
Becky Nagel serves as vice president of AI for 1105 Media specializing in developing media, events and training for companies around AI and generative AI technology. She also regularly writes and reports on AI news, and is the founding editor of PureAI.com. She's the author of "ChatGPT Prompt 101 Guide for Business Users" and other popular AI resources with a real-world business perspective. She regularly speaks, writes and develops content around AI, generative AI and other business tech. She has a background in Web technology and B2B enterprise technology journalism.