As companies continue to introduce their new technologies and products incorporating artificial intelligence and machine learning data models, the need for analytic deployment competency grows even larger. Companies interested in launching their own products and services driven by complex data models are going to face major development, deployment, and productions challenges based on the incongruent nature of the programing languages and complex schemas involved.
Stu Bailey, CTO from Open Data Group, shares how enterprises can make analytic deployment a core competency vital to competing successfully in today’s business environment.
- Q. How can companies make sure they’re getting their analytic products out?
A. In order for these products to get out quickly and efficiently, data science & IT teams will need to come together or risk the product’s success. With Analytic Deployment Technology, IT and data scientists are brought together to deploy & scale more models, more often, with greater efficiency than ever. It’s a necessary step to compete with companies like Apple and Google as they try to extend their leadership position in machine learning and artificial intelligence products and services.
- Q. What is the importance of Analytic Deployment Technology?
A. We believe that analytic models, especially in the era of machine learning and artificial intelligence, represent a huge opportunity for data scientists and IT professionals to drive substantial revenue for their company. The ability to deploy models, more often, at global scale, with more frequent iterations is becoming a requirement to compete and win in market across almost every vertical. Our mission reflects this opportunity and the need to make analytic deployment a core competency for your business.
- Q. Why is today’s analytic deployment process untenable?
A. Today, data scientists are often boxed in by production requirements, which not only slows down their work and impacts quality, but it hurts morale as well. Sometimes, a company will allow the data scientist to ignore the production constraints, but this really only delays the process and IT hand-off when recoding.
Even more, to deploy with machine learning and artificial intelligence models requires leveraging of complex math and multiple inputs/outputs – most IT environments were not designed to handle the complexities of machine learning and artificial intelligence. To make the process more challenging, today’s technology pace has presented the need to iterate on these models more frequently than ever before.
- Q. How can these problems in deployment be fixed?
A. Analytic deployment technology solves for all of these problems and helps enterprise companies make analytic deployment a core competency.
For models to deployed in the best way possible, it’s important for scientists to be scientists – operating with tools and techniques that help them build the best models possible. Analytic Deployment Technology starts with the model hand-off from Data Scientist to IT teams – no matter what coding language the teams use. It’s important that companies aren’t using monolithic enterprise deployment. The technology is there to fit on existing technology environments and plug as appropriate to technology you are using today, and technology you might adopt in the future.
Only with a language-agnostic analytic deployment engine, can you build the competency to move any analytic or predictive model into production efficiently, then update the model as often as needed.
Why do we need to address the problems with analytic deployment?
As organizations continue to gather massive data sets and develop more advanced analytic models to extract value from them, they are increasingly encountering barriers to production deployment, scale, and servicing. Software focused on analytic deployment to enterprise production environments is needed to enable Analytic and IT team success that drives ROI for their businesses.
We are fully transitioned from the era of big data management to the era of big data activation where analytic and predictive models are transforming the business world in every vertical. And it’s not just about the analytic teams. IT professionals play a huge role in this opportunity too. They won’t be able to succeed if a productive partnership between IT and their analytic team counterparts fails to emerge. The mission is clear – we must bring IT and Analytic Professionals together to deploy & scale more models, more often, with better quality and greater efficiency than ever.
Stu Bailey is the Chief Technology Officer of Open Data Group (ODG). He has been focused on analytic and data intensive distributed systems for over two decades. Stu was the founder of Infoblox, serving as Chief Scientist and CTO.
