Specifically, this application was designed to help analysts get personalized recommendations (based on their own preference settings, ratings provided by their co-workers) for stories they need to analyze as part of their daily work.
Brent Wodicka from AIS described this application in an earlier blog post.
A few key points from my conversation with Steve:
- We were able to refactor the solution to run completely in AzureGov by leveraging ML Server and Cosmos DB.
- ML Server provides a robust environment for executing R- and Python-based algorithms. It comes with a number of built-in algorithms. Additionally, customers can bring popular open-source algorithms into the mix. We used an open-source topic modeling library for our application.
- This solution represents an interesting hybrid of IaaS (ML Server running on a AzureGov VM) and PaaS services.
My full discussion with Steve is below. I also want to thank the AIS team members who worked hard work to build this application including Brent Wodicka, Jim Strang, Nisha Patel, and Nicholas Mark.
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