Category: Artificial Intelligence

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 […]

Introduced at AWS re:Invent in 2017, Amazon SageMaker provides a fully managed service for data science and machine learning workflows. One of the important parts of Amazon SageMaker is the powerful Jupyter notebook interface, which can be used to build models. You can enhance the Amazon SageMaker capabilities by connecting the notebook instance to an […]

Deep learning has revolutionized how we process and handle real-world data. There are many types of deep learning applications, including applications to organize a user’s photo archive, make book recommendations, detect fraudulent behavior, and perceive the world around an autonomous vehicle. In this post, we’ll show you step-by-step how to use your own custom-trained models […]

The AWS Deep Learning AMIs are now available in four new AWS Regions: China (Beijing) operated by Sinnet, Europe (Frankfurt), Asia Pacific (Singapore), and Asia Pacific (Mumbai). The Amazon Machine Images (AMIs) provide provide machine learning practitioners with the infrastructure and tools to accelerate deep to quickly start experimenting with deep learning models. The AMIs […]

We’re excited to update the AWS Deep Learning AMIs with significantly faster training on NVIDIA Tesla V100 “Volta” GPUs across many frameworks, including TensorFlow, PyTorch, Keras, and the latest Apache MXNet 1.0 release. There are two main flavors of the AMIs available today. The Conda-based AWS Deep Learning AMI packages the latest point releases of […]

Earlier this week, AWS announced the availability of Model Server for Apache MXNet, an open source component built on top of Apache MXNet for serving deep learning models. Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning. With Model Server for Apache MXNet, engineers are […]

Today, Amazon Web Services (AWS), Facebook and Microsoft are pleased to announce that the Open Neural Network Exchange (ONNX) format is production ready. ONNX is an open standard format for deep learning models that enables interoperability between deep learning frameworks such as Apache MXNet, Caffe2, Microsoft Cognitive Toolkit, and PyTorch. ONNX 1.0 enables users to […]

Haptik is India’s first personal-assistant app. Users can use the app to plan travel, check in for flights, book taxis, and set reminders. And of all the different features, the most important and frequently used is the Reminders feature. People use Haptik to set wake-up calls, set up reminders to drink water, call people at different times, send greetings to others for different occasions, and much more. Through the reminders feature, users will receive notifications on the app along with a phone call at a requested time, relating the reminder message. In this post, we will cover how we use machine learning and text-to-speech (TTS) to set reminders for users – to call them at the given time to remind them of their tasks. We will cover how Amazon Polly helped us make personalized calls to our users and helped us scale our reminders feature to millions of users.

Today AWS announced contributions to the milestone 1.0 release of the Apache MXNet deep learning engine and the introduction of a new model serving capability for MXNet. These new capabilities (1) simplify training and deploying deep learning models, (2) enable implementation of cutting-edge performance enhancements, and (3) provide easy interoperability between deep learning frameworks. In […]