AWS Machine Learning Blog

Amazon SageMaker now supports PyTorch and TensorFlow 1.8

Starting today, you can easily train and deploy your PyTorch deep learning models in Amazon SageMaker. This is the fourth deep learning framework that Amazon SageMaker has added support for, in addition to TensorFlow, Apache MXNet, and Chainer.  Just like with those frameworks, now you can write your PyTorch script like you normally would and […]

Customers can now use Amazon Translate, a neural machine translation service, in the AWS GovCloud (US) Region

For AWS government customers and organizations in government-regulated industries it is critical to communicate with people in their preferred languages. CSA Research shows that 51 percent of consumers prefer content in their own language, even if the language quality is less than perfect. To help our government customers and organizations in government-regulated industries, we are […]

Open Influence uses Amazon Rekognition to enhance its influencer marketing platform

Open Influence is an end-to-end influencer marketing platform that uses artificial intelligence to enable global brands and agencies to quickly and easily identify relevant influencers.  Influencer marketing has become a popular way for corporate brand marketers to work with influential leaders on social media to reach a specific audience. These influencers are critical in executing […]

Creating virtual guided navigation using a Question and Answer Bot with Amazon Lex and Amazon Alexa

If our users are using Question and Answer Bot (QnABot), and they are getting the right answers for their questions, what else can you do for them? Now you can create a bot to guide them through a series of logically connected answers. This means, for example, that the bot can guide them through the […]

Create a questionnaire bot with Amazon Lex and Amazon Alexa

In the Create a Question and Answer Bot with Amazon Lex and Amazon Alexa blog post, we showed you how you could create a QnABot (pronounced “Q and A Bot”) for a situation in which your users have questions and you have answers. Now, what if this situation were reversed? What if you could ask […]

Video analytics in the cloud and at the edge with AWS DeepLens and Kinesis Video Streams

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. Yesterday we announced the integration of AWS DeepLens with […]

Analyze US census data for population segmentation using Amazon SageMaker

August 2021: Post updated with changes required for SageMaker SDK v2, courtesy of Eitan Sela, Senior Startup Solutions Architect In the United States, with the 2018 midterm elections approaching, people are looking for more information about the voting process. This blog post explores how we can apply machine learning (ML) to better integrate science into […]

VidMob combines computer vision and language AI services for data-driven creative asset production

VidMob is a social video creation platform that marketers of all sizes can use to develop personalized advertising communications at scale. VidMob uses machine learning (ML) to power its SaaS application. This application uses metadata extraction and sentiment analysis to provide marketers with actionable insights into which creative assets resonate with their intended audience, and […]

AWS internal use-case: Evaluating and adopting Amazon SageMaker within AWS Marketing

We’re the AWS Marketing Data Science team. We use advanced analytical and machine learning (ML) techniques so we can share insights into business problems across the AWS customer lifecycle, such as ML-driven scoring of sales leads, ML-based targeting segments, and econometric models for downstream impact measurement. Within Amazon, each team operates independently and owns the […]