Data analytics is increasingly being brought to bear to treat human disease, but as more and more health data is stored in computer databases, one significant challenge is how to perform analyses across these disparate databases. In this post I take a look at the Observational Health Data Sciences and Informatics (or OHDSI, pronounced “Odyssey”) program that was formed to address this challenge, and which today accounts for 1.26 billion patient records collectively stored across 64 databases in 17 countries.
Tools like Apache Spark bring scale to machine learning, and Cloudera Data Science Workbench brings Spark to data scientists. What happens when a data scientist wants to burst into the cloud to forge models at scale? Cloudera Altus, that’s what.
We’ve heard it a hundred times: big data is here, software is free and open,
Cloudera Data Science Workbench (CDSW) provides data science teams with a self-service platform for quickly developing machine learning workloads in their preferred language, with secure access to enterprise data and simple provisioning of compute. Individuals can request schedulable resources (e.g. compute, memory, GPUs) on a shared cluster that is managed centrally.
While self-service provisioning of resources is critical to the rapid interaction cycle of data scientists, it can pose a challenge to administrators.
This article shows how to build and publish a customized Docker image for usage as an engine in Cloudera Data Science Workbench. Such an image or engine customization gives you the benefit of being able to work with your favorite tool chain inside the web based application.
Cloudera Data Science Workbench (CDSW) enables data scientists to use their favorite tools such as R, Python, or Scala based libraries out of the box in an isolated secure sandbox environment.