Category: Artificial Intelligence

This post is contributed by Xiaoyan Zhang, a Data Scientist from TalkingData. TalkingData is a data intelligence service provider that offers data products and services to provide businesses insights on consumer behavior, preferences, and trends. One of TalkingData’s core services is leveraging machine learning and deep learning models to predict consumer behaviors (e.g., likelihood of […]

This article was contributed by Josiah Davis, Charles Frenzel, and Chen Wu. TorchServe is a model serving library that makes it easy to deploy and manage PyTorch models at scale in production environments. TorchServe removes the heavy lifting of deploying and serving PyTorch models with Kubernetes. TorchServe is built and maintained by AWS in collaboration […]

Today more and more companies are taking a personalized approach to content and marketing. For example, retailers are personalizing product recommendations and promotions for customers. An important step toward providing personalized recommendations is to identify a customer’s propensity to take action for a certain category. This propensity is based on a customer’s preferences and past […]

In this tutorial, I will explain how to deploy an interactive live events solution with which speakers can present to a large pre-selected audience, and moderators can screen attendees to participate in the broadcast. This interactive live events solution, built with the Amazon Chime SDK, addresses many of the shortcomings of traditional online meeting platforms […]

Customers on AWS deploy trained machine learning (ML) and deep learning (DL) models in production using Amazon SageMaker, and using other services such as AWS Lambda, AWS Fargate, AWS Elastic Beanstalk, and Amazon Elastic Compute Cloud (Amazon EC2) to name a few. Amazon SageMaker provides SDKs and a console-only workflow to deploy trained models, and […]

Many AWS customers—startups and large enterprises—are on a path to adopt machine learning and deep learning in their existing applications. The reasons for machine learning adoption are dictated by the pace of innovation in the industry, with business use cases ranging from customer service (including object detection from images and video streams, sentiment analysis) to […]

If you work in data science, you might think that the hardest thing about machine learning is not knowing when you’ll be done. You start with a problem, a dataset, and an idea about how to solve it, but you never know whether your approach is going to work until later, after you’ve wasted time. […]

I like prototyping on my laptop, as much as the next person. When I want to collaborate, I push my code to GitHub and invite collaborators. And when I want to run experiments and need more compute power, I rent CPU and GPU instances in the cloud, copy my code and dependencies over, and run […]

As the dust settles after another re:Invent closes, I wanted to put together a quick summary of all the open source-related announcements that happened in the run up to this year’s re:Invent and the week itself. If you are interested in open source in mobile web development, devops, containers, security, big data and data analytics, […]

Training heavy-weight deep neural networks (DNNs) on large datasets ranging from tens to hundreds of GBs often takes an unacceptably long time. Business imperatives force us to search for solutions that can reduce the training time from days to hours. Distributed data-parallel training of DNNs using multiple GPUs on multiple machines is often the right […]