FastScore Introduces Jupyter Integration

Maximize flexibility to design models and ensure they are ready for deployment.

We are excited to make placing models into FastScore simple.  Watch a step-by-step demo of our new Jupyter integration with Matthew Mahowald, Product Manager/Data Scientist.

A simple restful API with Jupyter allows you to verify how models are behaving before they are uploaded into FastScore engines.  Prepare and upload models, validate data schemas, identify potential production failures and errors, leverage your full data science stack, including libraries like Pandas and data.table, as well as validate, score and gain feedback.  Watch and get answers to our most frequently asked questions, and more.

  • What languages does the Jupyter platform support for FastScore?
  • Can I check and validate my models before uploading them to FastScore engines?
  • How can I ensure my model deploys before I hand it to the production team?

Jupyter integration allows the data science team maximum flexibility to design their models in familiar environments while simultaneously ensuring they are ready for deployment.   With Jupyter and FastScore you can test locally, and deploy globally.

Tagged: data science, FastScore, Open Data Group, Analytic Deployment, Jupyter

Analytic Deployment via FastScore Enables an Analytic Operations Center

Simultaneous analytic iteration and deployment with FastScore.  

In our first post in a series of video blogs, listen in as George from our engineering staff takes Brooke from our customer team through a demo of FastScore and creates an Analytic Operation Center.  In the demo, you will see two gradient boosting machine models deployed and scored in real time.  Both model instances are deployed in FastScore, then the two model inputs and outputs are combined in a dashboard using Grafana - and we can start to monitor the analytics scoring as well as some key performance metrics of the deployment.  Watch as they discuss several interesting concepts including:

  • How can you quickly change models in production from Python to R?  
  • What happens to the compute resources when I change model languages?
  • How can I leverage more analytic engines to increase scoring rates?
  • Are there differences in running models in Azure vs AWS?

Centralized deployment, iteration and monitoring of analytics enables an Analytic Operation Center for the business - a single place to understand, manage and extract value from the data science investment.

Tagged: data science, FastScore, Open Data Group, Analytic Deployment, python, analytic operation center

ASA Datafest

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.

Tagged: analytics, data science, datafest, Open Data Group, Analytic Deployment

Analytic Deployment Stacks and Frameworks (Part 1): Motivation

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”.

Tagged: analytic engines, Analytical Operations

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

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”)

Tagged: data science, Deployment, Model Deployment, scoring engine, Analytic Deployment

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. 

Tagged: FastScore, webinar

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. 

Tagged: analytics, organization, sales

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.  

Tagged: analytic engines, analytics, data science, Deployment, FastScore, Model Deployment, scoring engine

Histograms and High Level Languages at StrangeLoop

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.

Tagged: data science, Jim Pivarski, PFA

7/20 Meet-Up: Model Deployment with Bob Grossman

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.

Tagged: Analytical Operations, AnalyticOps, Model Deployment, Predictive Model Analytics, Robert Grossman