Webinar - Create and Deploy Gradient Boosted Machines

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. 

Our webinar will focus on providing 3 key takeaways: 

  • Learn how to create a gradient boosting machine using SciKit Learn and Python
  • Understand the steps required to transform features, train, and deploy a GBM using FastScore, a language agnostic analytic engine 
  • See a live demo of a GBM analyzing auto insurance risk 

Jan 25, 2017 - 10am PST (1pm EST)

Welcome Michael Barton - Director of Sales

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. 

There are many interesting points of view on when to hire sales, and in what quantity and capacity.  

Bringing Michael on board allows us to scale with the current and future demand for the product, demos and projects we are seeing from the market today.  It's an exciting time at Open Data Group.

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

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.  

v1.2 brings a unified capability to deploy analytics from a variety of model producers including R, Python, and Java.  The release also includes new input/output stream type supports, and some pretty major performance improvement - 3-5x prior versions.  Check the detailed Release Notes for more information.

It's an exciting time in the analytics market - the number of model producers continues to grow, as do the use cases that can leverage data science.  FastScore continues to be data science language and data platform agnostic - if you can build a model, we can deploy it.