Amazon Web Services (AWS) yesterday rolled out yet another new set of capabilities for its SageMaker end-to-end machine learning development and deployment service, including new streaming algorithms and batch job improvements. The company made the announcement at the New York edition of its AWS Summit series.
The new Streaming Algorithms feature is designed to allow users to accelerate their own algorithms by streaming large volumes of training data from the Amazon Simple Storage Service (Amazon S3). This capability should speed up training of machine learning algorithms dramatically, said Matt Wood, director of Machine Learning at AWS.
"We see a dramatic decrease in time to train models and put them into production," Wood, told conference attendees. Streaming Algorithms is available now to users of the popular TensorFlow open source machine learning framework, he said, promising support for additional frameworks in the future.
The new Batch Transform feature, which will also pull directly from S3 storage, is designed to allow enterprises to handle big jobs without breaking up the data with an API call. In other words, it allows cloud operators to transform data dumps in batches.
Introduced last November at there: Invent 2017 conference, SageMaker is a fully managed service designed to enable data scientists and developers to build, train and deploy machine learning models. Less than a year old, SageMaker is iterating at a breakneck pace, with new features and integrations announced almost monthly.
"Our original vision for AWS was to enable an individual in his or her dorm room or garage to have access to the same technology, tools, scale, and cost structure as the largest companies in the world," said Swami Sivasubramanian, VP of Machine Learning at AWS, at last November's unveiling. "Our vision for machine learning is no different. We want all developers to be able to use machine learning much more expansively and successfully, irrespective of their machine learning skill level. Amazon SageMakerremoves a lot of the muck and complexity involved in machine learning to allow developers to easily get started and become competent in building, training, and deploying models."
The company also announced improvements to Amazon Translate and Amazon Transcribe. Translate now supports the Japanese, Russian, Italian, traditional Chinese, Turkish, and Czech languages, with support for Dutch, Swedish, Polish, Danish, Hebrew, and Finnish coming soon. Transcribe now supports Channel Synthesis, which can take multi-channel audio streams and construct a single transcription from them.
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Amazon SageMaker removes a lot of the muck and complexity involved in machine learning to allow developers to easily get started and become competent in building, training, and deploying models."