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Favorite One of the challenges in data science is getting access to operational or real-time data, which is often stored in operational database systems. Being able to connect data science tools to operational data easily and efficiently unleashes enormous potential for gaining insights from real-time data. In this post, we
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Shared by AWS Machine Learning December 1, 2020
Favorite If your contact center is serving calls over the internet, network metrics like packet loss, jitter, and round-trip time are key to understanding call quality. In the post Easily monitor call quality with Amazon Connect, we introduced a solution that captures real-time metrics from the Amazon Connect softphone, stores
Read More Shared by AWS Machine Learning December 1, 2020
Favorite Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to your applications. Today, we’re excited to launch Japanese, Korean, and Brazilian Portuguese language support for Amazon Transcribe streaming. To deliver streaming transcriptions with low latency for these languages, we’re also announcing
Read More Shared by AWS Machine Learning December 1, 2020
Favorite This is a guest post authored by Rebecca Owens and Julian Hernandez, who work at Genesys Cloud. Legacy technology limits organizations in their ability to offer excellent customer service to users. Organizations must design, establish, and implement their customer relationship strategies while balancing against operational efficiency concerns. Another factor
Read More Shared by AWS Machine Learning December 1, 2020
Favorite Ever since Amazon SageMaker was introduced at AWS re:Invent 2017, customers have used the service to quickly and easily build and train machine learning (ML) models and directly deploy them into a production-ready hosted environment. SageMaker notebook instances provide a powerful, integrated Jupyter notebook interface for easy access to
Read More Shared by AWS Machine Learning December 1, 2020
Favorite Amazon SageMaker Studio notebooks and Amazon SageMaker notebook instances are internet-enabled by default. However, many regulated industries, such as financial industries, healthcare, telecommunications, and others, require that network traffic traverses their own Amazon Virtual Private Cloud (Amazon VPC) to restrict and control which traffic can go through public internet.
Read More Shared by AWS Machine Learning December 1, 2020
Favorite Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps required to build, train, tune, debug, deploy, and monitor models. In this post, we demonstrate how you can create
Read More Shared by AWS Machine Learning November 28, 2020
Favorite Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up SageMaker Studio notebooks to explore datasets and build models. On October 27, 2020, Amazon released a custom images feature that allows you
Read More Shared by AWS Machine Learning November 25, 2020
Favorite Every day, financial organizations need to analyze news articles, SEC filings, and press releases, as well as track financial events such as bankruptcy announcements, changes in executive leadership at companies, and announcements of mergers and acquisitions. They want to accurately extract the key data points and associations among various
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Favorite This is a joint blog post with AWS Solutions Architects, Jorge Alfaro Hidalgo and Mauricio Zajbert, and Chester Perez, the Contact Center Manager at Xpertal. Fomento Económico Mexicano, S.A.B. de C.V. (FEMSA) is a Mexican multinational beverage and retail company headquartered in Monterrey, Mexico. Fomento Económico Mexicano, S.A.B. de
Read More Shared by AWS Machine Learning November 25, 2020