Streamlining deployment into production from any machine learning Platform written in any language.
Monitoring designed and built from the ground up for the unique characteristics and sensitivities of machine learning models.
Easy access for Data Scientists and MLOps and Operations professionals to be notified and take action to ensure that models are continuously delivering expected results throughout their lifecycle.
Fully governed environments, maintaining full lineage and ensuring compliance and reducing risk from the whole process of managing machine learning in production.
DataRobot MLOps allows AI and MLOps teams to embed cutting edge predictive models in an efficient and value-driven way.
See What MLOps Can Do for AI and Machine Learning Teams
Three Key Feature Sets
Unleash the ability to work and experiment with different types of models created on any platform and in any language inside a single MLOps solution.
- Real-time predictions
- Batch predictions
- Service health monitoring
- Time series predictions
- Image and geospatial data types
- Java scoring code
- Portable docker image
Operating at Scale
Use and build upon the foundation you already have, regardless of run-time environment.
- Monitoring diverse prediction environments
- Audit logs
- Versioning and lineage
- Change approval workflows
- No-code prediction GUI
- Value and use case tracking
- Repo integration
Making Machine Learning Trustworthy
Deploy reliable, trustworthy and unbiased models.
- Data drift analysis
- Accuracy analysis
- Anomaly warnings
- Prediction explanations
- Champion/Challenger gates into production
- Humble AI – built in mechanisms ensuring trust in your models
- Prediction intervals
Unique approach to environment-agnostic MLOps Architecture
I really think using DataRobot MLOps is the reason why we didn’t have to stress about it [COVID] as much as other companies have. The only reason we were comfortable in doing that is that when we see performance changes via MLOps we can throw everything automatically back into DataRobot AutoML and see what it tells us in terms of model comparison and see what we need to do based on where we’re at at that point of time.
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.