AWS Machine Learning Blog

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. Although you can disable direct internet […]

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 data sources for exploration and analysis. […]

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 to consider is the constant evolution […]

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 availability of Amazon Transcribe streaming in […]

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 them in Amazon Elasticsearch Service (Amazon […]

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 explore using Amazon SageMaker to analyze […]

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 a SageMaker Studio domain and user […]

With less than a week to re:Invent 2020, we are feeling the excitement and thrill, and looking forward to seeing you all at the world’s premier cloud learning event. As always, artificial intelligence (AI) and machine learning (ML) continue to be on the list of top topics with our customers and partners. We’re making it […]

We’re excited to announce that you can now measure the accuracy of forecasts for individual items in Amazon Forecast, allowing you to better understand your forecasting model’s performance for the items that most impact your business. Improving forecast accuracy for specific items—such as those with higher prices or higher costs—is often more important than optimizing […]

¡Amazon Lex lanza soporte para español para América Latina! Amazon Lex startet auf Deutsch! Amazon Lex is a service for building conversational interfaces into any application using voice and text. Starting today, Amazon Lex supports Latin American Spanish and German. Now you can easily create virtual agents, conversational IVR systems, self-service chatbots, or application bots […]