Following the Acquisition of ParalleIM, DataRobot Augments Its Enterprise AI Platform with Centralized Deployment, Monitoring and Governance for Any Model Anywhere
DataRobot, the leader in enterprise AI, unveiled DataRobot MLOps, a robust machine learning operations (MLOps) solution for deploying, monitoring, and managing machine learning models across the enterprise. MLOps combines DataRobot’s existing model management and monitoring solution with capabilities from MLOps category leader ParallelM, which DataRobot acquired in June. DataRobot’s new MLOps offering provides a centralized hub for deployment, monitoring and governance of models created from a variety of tools. As a result, organizations will be able to accelerate the time it takes them to deploy and scale machine learning-based services in production, to finally realize tangible value from their machine learning initiatives.
Despite the massive investments in data science teams and infrastructure, many companies have not been able to derive measurable value from AI projects. According to industry analysts, only a fraction of machine learning models make it into production. The few models that do make it into production do not have the necessary monitoring and governance that’s required to ensure they are accurate and consistent throughout changing market or environmental conditions. Effective and responsible use of AI requires a modern and centralized system to automate the deployment, monitoring, management, and governance of both models and projects through every step of the AI production lifecycle.
DataRobot’s previous model management and monitoring solutions, embedded within its DataRobot automated machine learning product, made it simple for customers to operationalize models and continuously monitor their success. ParallelM’s technology deploys and manages machine learning models built on a variety of machine learning platforms onto customer-managed environments, including Kubernetes and Spark. By combining the two, DataRobot’s new MLOps product offers advanced, real-time monitoring and centralized management and governance for models created using leading machine learning platforms, frameworks, and languages, making it a completely open platform.
“We created ParallelM and MLOps to help the industry accelerate the path to value that should be derived from machine learning by scaling the operationalization of models across any execution environment. We are excited to release DataRobot MLOps which will rapidly unleash the full potential of AI for a much larger set of customers,” said Sivan Metzger, Managing Director of MLOps at DataRobot and former CEO of ParallelM. “With this release, and for the first time, we can deliver on our customer’s needs to have centralized and automated deployment, monitoring and governance for all their production machine learning models”
DataRobot customers now have a single platform to deploy models and see the status of all production models independent of where they were created or where they are deployed. Real-time dashboards allow users to identify models that should be re-trained or replaced to prevent production issues or poor business performance. In addition, centralized and embedded governance allows organizations to maintain control over AI projects, comply with government regulations, and reduce risk from access or changes to production models.
“With monitoring dashboards that are alive with real-time information about all AI and machine learning models across the enterprise, this product supports the aggressive AI deployment plans of our customers,” said Phil Gurbacki, senior vice president of product and customer experience at DataRobot. “We’re excited to be taking our model monitoring and management capabilities to the next level with the introduction of MLOps. Adding this product to our platform further underscores our commitment to automating the entire AI lifecycle, letting customers derive the most value from AI.”
In addition to DataRobot MLOps, DataRobot also announced a new $206 million Series E funding round today.