Your Center of Excellence for Production AI
In a volatile world, your machine learning models can turn quickly from assets into liabilities. When faced with conditions not encountered in the training data, your models will make inaccurate and unreliable predictions that will undermine consumer trust and introduce risk to the business. Additionally, most machine learning deployment processes today are manual, complex, and span data science, business, and IT organizations, impeding the rapid detection and repair of model performance problems. To maintain current levels of AI adoption and scale in order to take advantage of new opportunities, every organization needs a better way to deploy and manage the lifecycle of all their production models holistically across the enterprise.
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. MLOps improves the overall quality of your models, using advanced automated machine learning health monitoring and accommodates changing conditions via continuous automated model competitions (aka ‘Champion/Challenger’ mechanisms). It also ensures that all centralized production machine learning processes work under a robust governance framework across your organization, leveraging and sharing the burden of production model management with the teams you already have in place.
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.
What people say
With MLOps, we were able to deploy both DataRobot and non-DataRobot models within minutes rather than weeks, enabling us to achieve a far faster time to value than with homegrown deployments. In addition, the monitoring capabilities ensure that our models are generalizing appropriately to new data. We have so far had 100% uptime on our deployments.