Category: Amazon SageMaker

Today we are excited to announce that you can deploy your custom models trained using Gluon to your AWS DeepLens. Gluon is an open source deep learning interface which allows developers of all skill levels to prototype, build, train, and deploy sophisticated machine learning models for the cloud, devices at the edge, and mobile apps. […]

Introduced at re:Invent 2017, Amazon SageMaker provides a serverless data science environment to build, train, and deploy machine learning models at scale. Customers also have the ability to work with frameworks they find most familiar, such as Scikit learn. In this blog post, we’ll accomplish two goals: First, we’ll give you a high-level overview of […]

Amazon SageMaker pre-built deep learning framework containers now support TensorFlow 1.5 and Apache MXNet 1.0, both of which take advantage of CUDA 9 optimizations for faster performance on SageMaker ml.p3 instances. In addition to performance benefits, this provides access to updated features such as Eager execution in TensorFlow and advanced indexing for NDArrays in MXNet. More […]

Machine learning (ML) methods for the field of computational chemistry are growing at an accelerated rate. Easy access to open-source solvers (such as TensorFlow and Apache MXNet), toolkits (such as RDKit cheminformatics software), and open-scientific initiatives (such as DeepChem) makes it easy to use these frameworks in daily research. In the field of chemical informatics, many […]

We are excited to launch a new feature for AWS DeepLens that allows you to import models trained using Amazon SageMaker directly into the AWS DeepLens console with one click. This feature is available as of AWS DeepLens software version 1.2.3. You can update your AWS DeepLens software by re-booting your device or by using […]

Today we’re launching Amazon SageMaker BlazingText as the latest built-in algorithm for Amazon SageMaker. BlazingText is an unsupervised learning algorithm for generating Word2Vec embeddings. These are dense vector representations of words in large corpora. We’re excited to make BlazingText, the fastest implementation of Word2Vec, available to Amazon SageMaker users on: Single CPU instances (like the […]

Amazon SageMaker uses throwaway keys, also called transient keys, to encrypt the ML General Purpose storage volumes attached to training and hosting EC2 instances. Because these keys are used only to encrypt the ML storage volumes and are then immediately discarded, the volumes can safely be used to store confidential data. Volumes can be accessed […]

Just as the goal of Amazon AI is to democratize machine learning with the development of platforms such as Amazon SageMaker, the goal of fast.ai is to level the educational playing field so that anyone can pick up machine learning and be productive. The fast.ai tagline is “Making neural nets uncool again.” This is not a play to decrease the popularity of deep neural networks, but instead to broaden their appeal and accessibility beyond the academic elites who have dominated the research in this area.

AWS customers have been requesting a way to log activity in Amazon SageMaker, to help you meet your governance and compliance needs. I’m happy to announce that Amazon SageMaker is now integrated with AWS CloudTrail, a service that enables you to log, continuously monitor, and retain account information related to Amazon SageMaker API activity. Amazon […]

Today we are launching Amazon SageMaker DeepAR as the latest built-in algorithm for Amazon SageMaker. DeepAR is a supervised learning algorithm for time series forecasting that uses recurrent neural networks (RNN) to produce both point and probabilistic forecasts. We’re excited to give developers access to this scalable, highly accurate forecasting algorithm that drives mission-critical decisions within Amazon. Just as […]