AWS News Blog
Category: Amazon SageMaker
Amazon SageMaker Ground Truth Keeps Simplifying Labeling Workflows
Launched at AWS re:Invent 2018, Amazon SageMaker Ground Truth is a capability of Amazon SageMaker that makes it easy for customers to efficiently and accurately label the datasets required for training machine learning systems. A quick recap on Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine […]
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 […]
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 […]
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 […]
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 Amazon SageMaker 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 […]
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 […]
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 Amazon SageMaker. Starting today, SageMaker is available in Asia Pacific (Tokyo)! 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 […]
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, […]