ODG is happy to announce our sponsorship and participation in two ASA Datafest events this spring. ODG will work with the Ohio State University in Columbus, OH and Loyola University in Chicago, IL to help students get the most from their weekends. Our relationship with Datafest started last year, where we both mentored and judged the 2016 event at Loyola University. It was a blast, and we were happy to see students really digging into the problem and finding unique solutions.
This is part 1 in a multi-part series discussing an approach to effective deployment of analytic models at scale.
It’s 2017. Your organization has been collecting valuable data for several years. The organization you work for is somewhere on the spectrum of analytic maturity from “we just hired our first data scientist” to “we are in the credit scoring business and have been developing critical analytics for decades”.
This is part 2 in a series discussing an approach to effective deployment of analytic models at scale. You can find part 1 here.
Our first abstraction intended to aide the coordination of analytic designers and analytic deployment is the model. As an abstract entity, a model has four main components.
- input (factors)
- output (scores and other data)
- state (including initial state, usually trained or fitted to historical data)
- the math (some times what is called the “scoring function”)
Is your organization ready to deploy analytic models at scale? Are your existing systems connected in the right ways to leverage the latest analytics capabilities? Join us for a live webinar detailing the creation and deployment of gradient Boosting machine models using Python, Kafka and FastScore. This webinar, led by Open Data’s Matthew Mahowald, will increase your understanding of the benefits of gradient boosting as well as the easiest way to deploy and maintain a live streaming gradient boosting machine model in production systems.
We are very excited to announce that Michael Barton joined Open Data Group today as Director of Sales. With any small company, hiring sales is an important step - it must be timed right with both product market readiness. Given the last 2 quarters of work on our product, and the reception of that work in the market, it's clear we needed help.
It's been a little while since we had a blog post, as we've been heads down on software development. We've been working on improving FastScore with our partners and customers, as well as the input we are receiving from the market. It's really exciting to announce FastScore v1.2 today, which marks the 3rd release in 6 months of the product. The feature velocity is gaining steam, as are the demos.
This year’s StrangeLoop conference is less than a week away and I’m psyched. This meeting with an odd name lies at the intersection of an odd blend of topics, including distributed systems, languages, and data science. It would be a natural place for me to talk about PFA, which covers all three, but instead I decided to talk about something new: a language of histogram aggregation called Histo·grammar.
Last Wednesday, Open Data Group had the opportunity to co-host a data science meet-up with DataScope, which manages the Data Science Chicago Meet-Up. We thoroughly enjoyed the experience and appreciate all the folks who came out for discussion and pizza. Bob Grossman, Open Data’s founder and Chief Data Scientist, introduced the concept of AnalyticOps (read CTO Stu Bailey’s posts on the same topic here) and the emerging core competency of deploying models. Bob was joined by Robert Nendorf from Allstate, who shared his views on a similar topic: DevOps for Data Science.
The GDC, a platform aimed at allowing unprecedented access to cancer research Data, launched June 6th as part of Vice President Joe Biden's Cancer Moonshot Initiative.
How is AnalyticOps different than DevOps or Data Science?
Well, it took four parts to get to this point, but we’ve used our time to discuss some of the abstractions that are required to understand the idea referred to as “AnalyticOps”. Our journey started with the abstract concept of “what is an analytic” while the second covered the operative concept of “deploying” the analytic with an analytic engine or deployment server.