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Favorite Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality
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Shared by AWS Machine Learning December 2, 2020
Favorite Yesterday, at AWS re:Invent, we announced AWS Panorama, a new Appliance and Device SDK that allows organizations to bring computer vision to their on-premises cameras to make automated predictions with high accuracy and low latency. With AWS Panorama, companies can use compute power at the edge (without requiring video
Read More Shared by AWS Machine Learning December 2, 2020
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
Read More 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
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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