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
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
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.
Favorite Knowledge changes, and knowledge of some topics changes faster than others. This has massive implications for knowledge management. Image from wikimedia commons Knowledge is not static. It changes and develops over time. It has a half-life, and that half-life seems to be shrinking as the world speeds up. Old
Favorite In 2006, the World Health Organisation published a regional KM strategy for Health in Africa. This suggested approach for developing a regional KM strategy could potentially be used in other political contexts. Image under CC licence from pxherecreated by Mohamed Hasan Knowledge Management is not just for industry or
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
Favorite Here’s a great example of a Knowledge Asset created to fill a critical knowledge need. Sometimes a KM team needs to take the lead in creating (or facilitating the creation of) a knowledge asset to fill a knowledge need. This is an example from the US Army where this
Favorite The video below is a product of the Olympic Games Knowledge Management program, as part of their methodology for transferring experience from one organising committee to another. The 27-minute video is introduced here, and was created during the Rio Olympics to describe the work of press photographers at the
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
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
Shared by AWS Machine Learning December 1, 2020
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