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Category: SageMaker

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Amazon SageMaker RL – Managed Reinforcement Learning with Amazon SageMaker

In the last few years, machine learning (ML) has generated a lot of excitement. Indeed, from medical image analysis to self-driving trucks, the list of complex tasks that ML models can successfully accomplish keeps growing, but what makes these models so smart? In a nutshell, you can train a model in several different ways of which […]

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Amazon SageMaker Ground Truth – Build Highly Accurate Datasets and Reduce Labeling Costs by up to 70%

In 1959, Arthur Samuel defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed”. However, there is no deus ex machina: the learning process requires an algorithm (“how to learn”) and a training dataset (“what to learn from”). Today, most machine learning tasks use a technique […]

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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 parallelism for Machine Learning and High Performance Computing: curious minds will enjoy the seminal paper (PDF) published in 2009 […]

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Amazon SageMaker Adds Batch Transform Feature and Pipe Input Mode for TensorFlow Containers

At the New York Summit a few days ago we launched two new features: a new batch inference feature called Batch Transform that allows customers to make predictions in non-real time scenarios across petabytes of data and Pipe Input Mode support for TensorFlow containers. SageMaker remains one of my favorite services and we’ve covered it […]

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Amazon SageMaker Automatic Model Tuning: Using Machine Learning for Machine Learning

Today I’m excited to announce the general availability of Amazon SageMaker Automatic Model Tuning. Automatic Model Tuning eliminates the undifferentiated heavy lifting required to search the hyperparameter space for more accurate models. This feature allows developers and data scientists to save significant time and effort in training and tuning their machine learning models. A Hyperparameter […]

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Amazon SageMaker Updates – Tokyo Region, CloudFormation, Chainer, and GreenGrass ML

Today, at the AWS Summit in Tokyo we announced a number of updates and new features for . Starting today, SageMaker is available in ! SageMaker also now supports CloudFormation. A new machine learning framework, Chainer, is now available in the SageMaker Python SDK, in addition to MXNet and Tensorflow. Finally, support for running Chainer […]

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New – Machine Learning Inference at the Edge Using AWS Greengrass

What happens when you combine the Internet of Things, Machine Learning, and Edge Computing? Before I tell you, let’s review each one and discuss what AWS has to offer. Internet of Things (IoT) – Devices that connect the physical world and the digital one. The devices, often equipped with one or more types of sensors, […]

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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 take a quick look at each of these updates. New Instance Types Amazon SageMaker customers now have additional […]

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March Machine Learning Madness!

Mid-march in the USA means millions of people watching, and betting on, college basketball (I live here but I didn’t make the rules). As the NCAA college championship continues I wanted to briefly highlight the work of Wesley Pasfield one of our Professional Services Machine Learning Specialists. Wesley was able to take data from kenpom.com […]

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Auto Scaling is now available for Amazon SageMaker

Kumar Venkateswar, Product Manager on the AWS ML Platforms Team, shares details on the announcement of Auto Scaling with Amazon SageMaker. With Amazon SageMaker, thousands of customers have been able to easily build, train and deploy their machine learning (ML) models. Today, we’re making it even easier to manage production ML models, with Auto Scaling […]

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