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Why Your Models Are Getting Lost in Translation

Ginger Phelps July 25, 2017
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Currently there are innumerable data languages that can be used for a wide range of analytic projects, and this amount will surely increase as new languages are being developed.

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Analytic Deployment Stacks and Frameworks (Part 2): Models Abstraction

Garrett Long February 8, 2017
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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”)

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FastScore v1.2 - We live to deploy your data science models

Garrett Long November 10, 2016
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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.  

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AnalyticOps: Part 3 - What is an Analytic Engine?

Garrett Long May 25, 2016
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How are Analytic Engines different than “deployment servers”?

Well, did you do last week's homework? If you used the emerging analytic standards of PFA or PPFA to create an analytic for trajectory planning, please send it to info@opendatagroup.com. You just might get a job offer! Heck, if you use the open source tools Aurelius or Titus to convert your R or Python models to PFA we’d love to hear it, and share your work with the growing community of analytic engine users.

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AnalyticOps: Part 1 - What is an Analytic Anyway?

Garrett Long May 16, 2016
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Or is an “analytic” just a fancy term for a “business rule”? 

Over the next few blog posts, I am going to tackle the subject of AnalyticOps, a relatively new function that organizations are going to have to implement and master to ensure maximum ROI on data science investments.  I’ll start with some basics of defining just what is “an analytic” and move through some key elements of implementing the tools (an Analytic Engine) and the competencies that make up AnalyticOps.

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