AWS Machine Learning Blog

With a few clicks in the Amazon SageMaker console or a few one-line API calls, you can now quickly search, filter, and sort your machine learning (ML) experiments using key model attributes, such as hyperparameter values and accuracy metrics, to help you more quickly identify the best models for your use case and get to […]

We are excited to announce the launch of two new sample projects: “Build a worker safety system” and “Who drinks the most coffee?” for AWS DeepLens. These sample projects provide guided instructions on how to use computer vision to build a complete machine learning application on AWS. The applications span the edge and the cloud, […]

The race is on! Start your engines! The AWS DeepRacer Scholarship Challenge from Udacity is now open for enrollment. As mentioned in our previous post, the AWS DeepRacer Scholarship Challenge program introduces you—no matter what your developer skill levels are—to essential machine learning (ML) concepts in a fun and engaging way. Each month, you put […]

This is a guest blog post by Dante Monaldo, co-founder and CTO of Pluto Money Pluto Money, a San Francisco-based startup, is a free money management app that combines banking, behavioral economics, and machine learning (ML) to guide Generation Z towards their financial goals in college and beyond. We’re building the first mobile bank designed […]

This is a guest blog post by Paul DeHart, co-owner and CEO, BlueToad. BlueToad, one of the leading global providers of digital content solutions, prioritizes innovation. Since 2017, we have enabled publishers (our customers) to provide audio versions of articles found in their digital magazines using Amazon Polly. We see that novel content experiences engage today’s […]

For a long time, it was only in science fiction that machines verbalized emotions. As of today, Amazon Polly is one step closer to changing that. As we work on Amazon Polly, we’re constantly seeking to improve the voices. We hope you’ll agree that today’s announcement of not only Neural Text-to-Speech (NTTS) but also the […]

Amazon Elastic Inference (EI) is a new service launched at re:Invent 2018. Elastic Inference reduces the cost of running deep learning inference by up to 75% compared to using standalone GPU instances. Elastic Inference lets you attach accelerators to any Amazon SageMaker or Amazon EC2 instance type and run inference on TensorFlow, Apache MXNet, and […]

Deep learning is changing the world. However, much of the foundation work, such as building containers, can slow you down. This post describes how you can build, train, and deploy fastai models into Amazon SageMaker training and hosting by using the Amazon SageMaker Python SDK and a PyTorch base image. This helps you avoid the […]

Customers often ask us how to get started when they do not have a deep data science and machine learning (ML) background. At AWS, our goal is to put ML in the hands of every developer and data scientist. AWS Training and Certification has partnered with edX to help you get started quickly and easily with ML with […]

Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning. It offers easy access to public and private human labelers, and provides them with built-in workflows and interfaces for common labeling tasks. Ground Truth can lower your labeling costs by up to 70% using automatic labeling. It works by training Ground […]