Your Models Were Accurate Yesterday. What About Today?
DataRobot MLOps provides a center of excellence for your production AI. This gives you a single place to deploy, monitor, manage, and govern all your models in production, regardless of how they were created or when and where they were deployed.
Monitor Your Existing Models in Just a Few Lines of Code
MLOps Agents. Centralized Monitoring and Lifecycle Management for Any Model
Deploy Your Models Today! View walkthroughs in DataRobot Community
Build and Run Your Models Anywhere
With MLOps, you can easily deploy any model to your production environment of choice, on-prem, in the cloud, or hybrid. By instrumenting MLOps monitoring agents, you can add monitoring to any existing production model already deployed.
MLOps makes it easy to deploy models written in any open-source language or library and expose a production-quality, REST API to support real-time or batch predictions. MLOps also offers built-in, write-back integrations to systems such as Snowflake and Tableau.
Automated Model Health Monitoring and Lifecycle Management
MLOps provides constant monitoring and production diagnostics to improve the performance of your existing models. Best practice ML monitoring right out of the box enables you to track service health, accuracy, and data drift to explain why your model is degrading. Build your own challenger models or use our industry-leading AutoML product to build and test them for you. MLOps give you constant evaluation and continuous learning capabilities that allow you to avoid surprise changes in model performance down the road — a situation becoming only too familiar in today’s dynamic and highly volatile world.
Embedded Governance, Humility, and Fairness
MLOps establishes a framework that helps to maintain the governance process for your AI projects across your entire organization. With customizable governance policies, you will have complete control over the access, review, approval workflows. It will also allow you to access the history of prediction activity and model updates for regulatory compliance. This means you always know what models have been created, how they are used and updated.
With the Humility feature, you can configure rules that enable models to recognize, in real-time, when they make uncertain predictions. Check out MLOps 101 by DataRobot to learn more.
MLOps 101: The Foundation for Your AI Strategy
Machine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability. Check out this MLOps guide by DataRobot.