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Support Automation Workflow (SAW) Runbook: AWSSupport-ManageWindowsService
published a month ago1 votes291 views
Build and Deploy Models Leveraging Cancer Gene Expression Data With SageMaker Pipelines and SageMaker Multi-Model Endpoints
published 2 months ago3 votes597 views
In this article we show how you can use SageMaker Pipelines and SageMaker Multi-Model Endpoints to efficiently orchestrate and deploy many models in a cost effective and efficient manner. We show how this can be leveraged in the context of cancer survival analysis to deploy many models that leverage gene expression signatures.
A Brief Primer to Onboarding Data To a Healthcare and Life Sciences Data Mesh Leveraging AWS Services
published 3 months ago4 votes1806 views
When leveraging a data mesh, Healthcare and Life Sciences organizations face unique compliance, regulatory, and other hurdles. This article describes 3 common challenges faced by pharmaceutical and healthcare companies when onboarding data to a data mesh, as well as solutions for how to address those challenges.
Push down queries when using the Google BigQuery Connector for AWS Glue
published 3 months ago1 votes440 views
The objective of this article is to describe how to use the Google BigQuery Connector for AWS Glue to build an optimized Extract, transform, and load (ETL) job by pushing down your own query to BigQuery. Query push down helps limiting the amount of data that needs to be scanned and transferred. Hence, it can optimize the cost of running the extraction query and the cost of data transfer.