Announcing DataRobot MLOps

The truth is that the work of data scientists cannot generate value if the models never make it to production. For data scientists writing custom models in languages like Python and R, the number of challenges for getting models into production can be overwhelming. Issues range from how to deploy model code on production systems, how to monitor performance, and how to deploy updates to models over time. From the business perspective, significant questions also loom such as, who has access to production models, what happens if a model stops performing as expected, and who needs to be involved in maintaining production models?

The answer to all these questions is an emerging industry practice, called machine learning operations, also known as MLOps. MLOps automates the capabilities to deploy, manage, and govern models in production environments. 

We are excited to announce the availability of DataRobot MLOps as part of the upcoming 5.2 release. This product has been years in the making. We introduced Model Management and Monitoring as part of the DataRobot Platform in 2018  and as this area evolved, we realized our customers also wanted to deploy custom models written in languages like Python and R, or on an array of platforms. In June, we acquired ParallelM, the pioneer in MLOps, to double down on our ability to accelerate and execute in the MLOps space. With the experience and technology of the ParallelM team, we have been able to expand and advance our MLOps vision very quickly. 

DataRobot MLOps inherits a lot of critical functionality that was developed over the past few years in the DataRobot Enterprise AI platform for advanced monitoring and model governance. So, you might ask yourself what's new? Here are the highlights.

  1. DataRobot MLOps is a stand-alone product - Now, anyone can get the excellent monitoring and management capabilities of DataRobot with MLOps for deployment, monitoring, management, and governance.
  2. Deployment to Kubernetes - Automatically containerize and deploy custom models to Kubernetes. If you know what K8s is, then you know that is excellent news.
  3. Support for Custom Models - Deploy and monitor models from a variety of languages and frameworks. This is for customers who want to build models using languages like Python and R or on other platforms or workbenches, but would still like to have robust monitoring and governance in place in one central location.
  4. Monitoring for any Environment - We took a page from ParallelM and created monitoring agents that allow us to collect data from models deployed outside of the DataRobot prediction servers. With this unique approach, customers can run models anywhere and monitor those models within a centralized dashboard.


Interested in learning more about DataRobot MLOps? Contact our team of MLOps experts for more information and insight: Or, request a demo to get started with DataRobot. 


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About the Author:

Dan Darnell is a leader on the product team at DataRobot where he works closely with the sales, marketing, and partner teams to bring MLOps to life for our customers through content and evangelism. Dan has been working in the analytics space for almost 20 years with the last eight years focused on machine learning technology. He holds an MBA from Carnegie Mellon University and a degree in Architectural Engineering from the University of Colorado at Boulder.