Company

Modern IT and Data Science in an Era of Analytic Deployment

Transitioning from a monolithic platform to a modular approach when building models

Modern IT and Data Science blog-1In this new article on InfoWorld from IDG, Open Data Group CTO Stu Bailey, discusses how the creation of new solutions within the data analytics industry have led to more innovative approaches when deploying models into production in his article with InfoWorld. These solutions have derived from an expansion of data and growth in demand for models from Data Scientists and IT.

“The mass amount of data that is being consumed everyday by data scientists has strengthened the demand for these intellectuals to use and build models off of this data.” – Stu Bailey.

A new solution to building models with a monolithic architecture, has been a modular approach. Previously, data scientists had to implement the use of tradeoffs when using a monolithic architecture due to the elements being interdependent. However, the creation of modular architectures has allowed for models to have the capability to be configured to fit all Data Science and IT needs due to its adaptable framework.  

Apart from changes within the architectural framework of building models, there has been a shift with the model interchange formats that Data Scientists and IT use as well. PMML, Predictive Model Markup Language, has been implemented to ease the scalability of scoring engines while ensuring safety. Although PMML is still used today to combat a few challenges recognized with the model interchange format, PFA, Portable Format for Analytics, has been growing in use. PFA has assisted with transitioning models from development to production while using a common language to ease the transition.

Each new solution has created a more innovative and efficient way to move models from development to production. Open Data Group has helped lead the way to a more innovative approach through the use of agnostic scoring engines. Agnostic scoring engines have removed common restrictions that many Data Scientists and IT face when deploying models into production. These engines are capable of being paired with any language, and do not require any tradeoffs. Continue reading to learn more about new solutions and innovative tools.

All ModelOp Blog Posts 

ModelOp Golden Ale Takes a Holiday – Part 2

ModelOp Golden Ale Takes a Holiday – Part 2

2 Minute Read By Greg Lorence Before we go much further, I feel obligated to state what is likely already obvious: I’m not all about that #InstaLife. All accompanying photography was snapped with little regard for composition, typically while stretching out from 4-6...

Q&A with Ben Mackenzie, AI Architect

Q&A with Ben Mackenzie, AI Architect

2 Minute Read By Ben Mackenzie & Linda Maggi How AI Architects are the Key to Operationalize and Scale Your AI Initiatives Each week we meet more and more clients who are realizing the importance of operationalizing the AI model lifecycle and who are dismissing...

Behind the scene of ModelOp by our Brewmasters- Part1

Behind the scene of ModelOp by our Brewmasters- Part1

2 Minute Read By Greg Lorence As a long-time homebrewer, when our President, Scott asked me, “wouldn’t it be cool if you and Jim brewed a beer to commemorate our rebrand later this year?” my reaction, after the immediate “heck yeah! Beer is awesome”, was honestly...

Open Data Group Officially Becomes ModelOp

Open Data Group Officially Becomes ModelOp

2 Minute Read By ModelOp Today, Open Data Group rebrands as ModelOp. Read more on Globe Newswire It is an exciting day for us, if only because people will stop asking “Why are you called Open Data Group?” after they understand what we do. More importantly the name...

Gartner & WIA Conferences Exit Poll

Gartner & WIA Conferences Exit Poll

2 Minute Read By Garrett Long As we continue into our “Year of Model Operations”, I thought it would be useful to highlight some of the key things I observed, learned and shared over the last few weeks at both the Gartner Data and Analytics Summit March 18-21, 2019 in...

Machine Learning Model Interpretation

To either a model-driven company or a company catching up with the rapid adoption of AI in the industry, machine learning model interpretation has become a key factor that helps to make decisions towards promoting models into business. This is not an easy task --...

Matching for Non Random Studies

Experimental designs such as A/B testing are a cornerstone of statistical practice. By randomly assigning treatments to subjects, we can test the effect of a test versus a control (as in a clinical trial for a proposed new drug) or can determine which of several web...

Distances And Data Science

We're all aware of what 'distance' means in real-life scenarios, and how our notion of what 'distance' means can change with context. If we're talking about the distance from the ODG office to one of our favorite lunch spots, we probably mean the distance we walk when...