Deploying Jupyter Notebooks

Teja Srivastasa has an example of deploying a Jupyter notebook for production use on AWS:

No one can deny how large the online support community for data science is. Today, it’s possible to teach yourself Python and other programming languages in a matter of weeks. And if you’re ever in doubt, there’s a StackOverflow thread or something similar waiting to give you the perfect piece of code to help you.

But when it came to pushing it to production, we found very little documentation online. Most data scientists seem to work on Python notebooks in a silo. They process large volumes of data and analyze it — but within the confines of Jupyter Notebooks. And most of the resources we’ve found while growing as data scientists revolve around Jupyter Notebooks.

Another option might be to use JupyterHub.

Related Posts

Platform Compatibility and SSDT

Ed Elliott walks us through platform compatibility in SQL Server Data Tools: Sometimes you don’t have the perfect development environment for SQL Server, sometimes you deploy to things like SQL Azure and want to test locally, for various reasons it is possible that you want to deploy to one version of SQL Server but set […]

Read More

Building Credit Scorecards

Andre Violante uses SAS to build credit scorecards and analyze credit data: For this analysis I’m using the SAS Open Source library called SWAT (Scripting Wrapper for Analytics Transfer) to code in Python and execute SAS CAS Action Sets. SWAT acts as a bridge between the python language to CAS Action Sets. CAS Action Sets are synonymous to libraries […]

Read More

Categories

February 2018
MTWTFSS
« Jan Mar »
 1234
567891011
12131415161718
19202122232425
262728