Content language: English
Sort by most recent
Build and Deploy Models Leveraging Cancer Gene Expression Data With SageMaker Pipelines and SageMaker Multi-Model Endpoints
published 6 months ago3 votes714 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.
published 7 months ago1 votes341 views
A Brief Primer to Onboarding Data To a Healthcare and Life Sciences Data Mesh Leveraging AWS Services
published 7 months ago5 votes2136 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.
published 7 months ago3 votes1073 views
published 7 months ago1 votes909 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.
published 8 months ago5 votes642 views