AI-powered code suggestions and security scans in Amazon SageMaker notebooks using Amazon CodeWhisperer and Amazon CodeGuru

By Admin 13/05/2023

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

Reduce Amazon SageMaker inference cost with AWS Graviton

By Admin 10/05/2023

AWS Machine Learning Blog Amazon SageMaker provides a broad selection of machine learning (ML) infrastructure and model deployment options to help meet your ML inference needs. It’s a fully-managed service and integrates with MLOps tools so you can work to scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce […]Continue reading

Announcing provisioned concurrency for Amazon SageMaker Serverless Inference

By Admin 10/05/2023

AWS Machine Learning Blog Amazon SageMaker Serverless Inference allows you to serve model inference requests in real time without having to explicitly provision compute instances or configure scaling policies to handle traffic variations. You can let AWS handle the undifferentiated heavy lifting of managing the underlying infrastructure and save costs in the process. A Serverless […]Continue reading

Host ML models on Amazon SageMaker using Triton: Python backend

By Admin 09/05/2023

AWS Machine Learning Blog Amazon SageMaker provides a number of options for users who are looking for a solution to host their machine learning (ML) models. Of these options, one of the key features that SageMaker provides is real-time inference. Real-time inference workloads can have varying levels of requirements and service level agreements (SLAs) in […]Continue reading

Run your local machine learning code as Amazon SageMaker Training jobs with minimal code changes

By Admin 26/04/2023

AWS Machine Learning Blog We recently introduced a new capability in the Amazon SageMaker Python SDK that lets data scientists run their machine learning (ML) code authored in their preferred integrated developer environment (IDE) and notebooks along with the associated runtime dependencies as Amazon SageMaker training jobs with minimal code changes to the experimentation done […]Continue reading

Connect Amazon EMR and RStudio on Amazon SageMaker

By Admin 18/04/2023

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

Create your RStudio on Amazon SageMaker licensed or trial environment in three easy steps

By Admin 11/04/2023

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