WATCH A DEMO
REQUEST DEMO

Blog

How to Monitor Your Machine Learning Models

Open Data Group October 2, 2018
0

In our previous blog we discussed how to deploy machine learning models into production successfully and efficiently. Getting models into production can be difficult, but that isn’t the only challenge you will face with machine learning models throughout their lifetime. Once the model has made it into production, it must be monitored in order to ensure that everything is working properly. There are many different roles involved in the getting the model into production. Similarly, the monitoring of each machine learning model requires the attention from many different perspectives to ensure that each aspect of the model is running accurately and efficiently.  Let’s take a closer look into the different perspectives we must consider when monitoring machine learning models, and why each is so important:

Read More

Increase Efficiency Between Data Science & IT When Deploying Models into Production

Open Data Group September 18, 2018
0

Deploying analytic models into production can often prove to be a difficult and tedious process. In an ideal world, data scientists create a model, they hand it off to IT, and IT puts that model into the production environment. Seems simple enough, right? However, as many data science and IT teams know, there are many complications that can turn this process from a simple one, to a highly complex back and forth.

Read More

Things to Consider When Integrating Machine Learning into Your Infrastructure

Open Data Group September 4, 2018
0

Machine learning has changed the way we leverage and apply analytic models, and it isn’t going away anytime soon. As more and more organizations bring machine learning into their analytic portfolio, benefits are becoming clearer. Machine learning increases efficiencies in many applications once it’s integrated into an organization’s infrastructure, but getting to that point comes with many challenges. Some challenges of incorporating machine learning into your company’s infrastructure can be technical, while others are strategic.

Read More

Technical Challenges of Model Deployment

Open Data Group July 25, 2018
0

Deploying analytic models can be a long, slow moving process with many obstacles along the way. Many models are abandoned before they ever make it into production because of inefficiencies that slow down or halt the entire process. To overcome the challenges of model deployment, we need to identify the problems and learn what causes them. Some of the top technical challenges organizations face when trying to deploy a model into production are:

Read More

Why Your Models Are Getting Lost in Translation

Ginger Phelps July 25, 2017
0

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.

Read More

The Evolution from PMML and PFA to Agnostic Scoring Engines

Ginger Phelps July 18, 2017
0

Before models can be placed into scoring engines and then into production, custom code has to be written for each model. This process is labor-intensive and often error-prone. After the model’s custom code is written, Data Scientists have to transport the model to IT.  

Read More

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

Garrett Long February 8, 2017
0

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”)

Read More

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

Garrett Long November 10, 2016
0

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.  

Read More

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

Garrett Long July 26, 2016
0

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.

Read More