BOSTON--(BUSINESS WIRE)--DataRobot, the leader in enterprise AI, today announced that it has acquired ParallelM, a Santa Clara, CA-based company that created the machine learning operations (MLOps) category, which helps organizations scale the deployment, management, and governance of machine learning in production using any ML platform on any cloud or on-premise environment.
Despite the massive investments in data science teams, platforms, and infrastructure, as well as a dramatic increase in the number of active AI projects, the value derived from these investments is grossly lacking due to the inability to deploy AI models into production. According to industry analysts, only a small percentage of AI models make it into production, and the few AI models that do severely lack the necessary governance and monitoring required to ensure the AI can be trusted. Effective and responsible use of AI requires a modern system to deploy, monitor, manage, and govern both models and projects through every step of the AI lifecycle.
"We've seen the value that is unleashed when companies overcome the barrier between data science and IT operations, which is required to put machine learning to work," said Sivan Metzger, CEO of ParallelM. "We are excited to join forces with DataRobot to help more customers worldwide finally see tangible value and ROI from their AI and machine learning projects and initiatives."
ParallelM pioneered the MLOps space with the launch of the MCenter platform in 2017. The MCenter platform helps organizations quickly deploy machine learning models on modern production infrastructures such as Kubernetes and Spark, either on-premise or on a cloud provider of their choice (including Amazon Web Services, Google Cloud Platform, and Azure). ParallelM also pioneered techniques for real-time monitoring and alerts tailored for the unique intricacies of models and the auditing of actions for models required in regulated industries.
Over the last 18 months, DataRobot has made a massive investment in model deployment, management, and monitoring capabilities. The company has built a large team dedicated to this and received a multitude of industry awards and recognition from leading analyst firms.
The acquisition and integration of ParallelM makes DataRobot the clear industry leader in MLOps and governance. As part of the acquisition, DataRobot will expand its platform's current model monitoring and management capabilities to include an industry-leading MLOps and Governance offering that accelerates the AI lifecycle for all projects regardless of ML platform, programming language, or deployment scenario: DataRobot’s fully managed AI Cloud, private cloud, multi-cloud, on-prem, or hybrid.
"Machine learning operations and governance is a must-have to become an AI-driven enterprise," said Jeremy Achin, CEO of DataRobot. “We are thrilled to have them on board, including having ParallelM CEO Sivan Metzger join our leadership team and run our MLOps and Governance business."
The ParallelM acquisition is DataRobot’s fourth acquisition in approximately two years.
About DataRobot
DataRobot is the leader in enterprise AI and the category creator and leader in automated machine learning. Organizations worldwide use DataRobot to empower the teams they already have in place to rapidly build and deploy machine learning models and create advanced AI applications. With a library of hundreds of the most powerful open source machine learning algorithms, the DataRobot platform encapsulates every best practice and safeguard to accelerate and scale data science capabilities while maximizing transparency, accuracy, and collaboration.
By making data scientists more productive and enabling the democratization of data science, DataRobot helps organizations transform into AI-driven enterprises. With offices around the globe, DataRobot is backed by $225 million in funding from top-tier firms, including New Enterprise Associates, Sapphire Ventures, Meritech, and DFJ. For more information, visit www.datarobot.com, and join the conversation on Twitter and LinkedIn.
