AWS Machine Learning Blog Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service. Models are packaged into containers for robust […]Continue reading
AWS Machine Learning Blog Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio. With this launch, you can programmatically […]Continue reading
Coronavirus | The Guardian They are issues of great interest to Europeans, but cannot be fully understood through a single national lens. The climate crisis, geopolitics, people trafficking, economic insecurity – the biggest challenges are not limited by national borders. Which is why, this September, the Guardian is launching Guardian Europe – a dedicated English-language […]Continue reading
AWS Machine Learning Blog Amazon SageMaker Pipelines is a fully managed AWS service for building and orchestrating machine learning (ML) workflows. SageMaker Pipelines offers ML application developers the ability to orchestrate different steps of the ML workflow, including data loading, data transformation, training, tuning, and deployment. You can use SageMaker Pipelines to orchestrate ML jobs […]Continue reading
AWS Machine Learning Blog Amazon SageMaker is a fully managed machine learning (ML) platform that offers a comprehensive set of services that serve end-to-end ML workloads. As recommended by AWS as a best practice, customers have used separate accounts to simplify policy management for users and isolate resources by workloads and account. However, when more […]Continue reading
AWS Machine Learning Blog Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). Studio provides a single web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. Lifecycle configurations are shell scripts triggered by Studio lifecycle […]Continue reading
AWS Machine Learning Blog Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. The first option is fast start, collaborative notebooks accessible within Amazon SageMaker Studio—a fully integrated development environment (IDE) for machine learning. You can quickly launch notebooks in Studio, easily dial […]Continue reading
AWS Machine Learning Blog RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions […]Continue reading
AWS Machine Learning Blog RStudio on Amazon SageMaker is the first fully managed cloud-based Posit Workbench (formerly known as RStudio Workbench). RStudio on Amazon SageMaker removes the need for you to manage the underlying Posit Workbench infrastructure, so your teams can concentrate on producing value for your business. You can quickly launch the familiar RStudio […]Continue reading
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