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Amazon SageMaker Neo – Train Your Machine Learning Models Once, Run Them Anywhere

Machine learning (ML) is split in two distinct phases: training and inference. Training deals with building the model, i.e. running a ML algorithm on a dataset in order to identify meaningful patterns. This often requires large amounts…
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Amazon Elastic Inference – GPU-Powered Deep Learning Inference Acceleration

One of the reasons for the recent progress of Artificial Intelligence and Deep Learning is the fantastic computing capabilities of Graphics Processing Units (GPU). About ten years ago, researchers learned how to harness their massive hardware…
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Amazon SageMaker Now Supports Additional Instance Types, Local Mode, Open Sourced Containers, MXNet and Tensorflow Updates

Amazon SageMaker continues to iterate quickly and release new features on behalf of customers. Starting today, SageMaker adds support for many new instance types, local testing with the SDK, and Apache MXNet 1.1.0 and Tensorflow 1.6.0. Let’s…

AWS Contributes to Milestone 1.0 Release and Adds Model Serving Capability for Apache MXNet

Post by Dr. Matt WoodToday AWS announced contributions to the milestone 1.0 release of the Apache MXNet deep learning engine including the introduction of a new model-serving capability for MXNet. The new capabilities in MXNet provide the following…