DataRobot MLOps focuses on allowing your organization to adopt AI at scale, on your terms. It enables production model deployment and monitoring, model lifecycle management, and robust model governance. In addition to lifecycle management for DataRobot models, DataRobot MLOps works with your custom models (e.g., R, Python, Java, Keras) and within your own infrastructure (AWS, GCP, Azure, etc.).
In this learning session, we review DataRobot MLOps with an emphasis on the valuable new features in the 6.2 release. For example, you can now containerize your DataRobot models and get predictions on your own infrastructure. Also, model monitoring now offers segmented analysis. Finally, in this release we added new governance workflows for managing all your models. Join us to learn about these features and more and ask questions of our hosts.
Have questions not answered during the learning session? Want to continue your conversation with Tristan, Chris, and Rajiv? You can send email to firstname.lastname@example.org or Post Your Comment here. We're looking forward to hearing from you!
We're excited to welcome Zepl and its employees and customers to DataRobot! The acquisition of Zepl and integration of its self-service data science notebook solution will provide additional flexibility for data scientists who prefer to code. In her blog article, Tricia Lee explains how you can check out Zepl today. Please have a look, give it a spin, and let us know what you think.