AWS Machine Learning Blog
Category: Amazon Redshift
Configure cross-account access of Amazon Redshift clusters in Amazon SageMaker Studio using VPC peering
With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon SageMaker Studio is the first fully integrated ML development environment (IDE) with a web-based visual interface. You can perform all ML development […]
Use the AWS Cloud for observational life sciences studies
In this post, we discuss how to use the AWS Cloud and its services to accelerate observational studies for life sciences customers. We provide a reference architecture for architects, business owners, and technology decision-makers in the life sciences industry to automate the processes in clinical studies. Observational studies lead the way in research, allowing you […]
Accessing data sources from Amazon SageMaker R kernels
Amazon SageMaker notebooks now support R out-of-the-box, without needing you to manually install R kernels on the instances. Also, the notebooks come pre-installed with the reticulate library, which offers an R interface for the Amazon SageMaker Python SDK and enables you to invoke Python modules from within an R script. You can easily run machine […]