MLOps helps make deployment easy, allowing operations teams to deploy models onto modern runtime environments in the cloud or on-premise. Users of the MLOps system do not have to know languages like Python and R to drag and drop a model into the system, create a container, and deploy the model to a production environment.
With MLOps monitoring in place, your teams can deploy and manage thousands of models, and your business will be ready to scale production AI. Monitoring includes service health, data drift, model accuracy, and proactive alerts that are sent to stakeholders, using a variety of channels like email, Slack, and Pagerduty.
MLOps is designed so that models can be updated frequently and seamlessly. Model lifecycle management supports the testing and warm-up of replacement models, A/B testing of new models against older versions, seamless rollout of updates, and failover procedures, as well as full version control for simple rollback to prior model versions.
MLOps provides the integrations and capabilities that teams need to ensure consistent, repeatable, and reportable processes for models in production. Key capabilities include access control for production models and systems, including integration to LDAP and role-based access control systems (RBAC), as well as approval flows, logging, version storage, and traceability of results for legal and regulatory compliance.
DataRobot MLOps allows IT Teams to manage cutting edge predictive models in an efficient and value-driven way.
See What MLOps Can Do for IT Leaders and Teams
Three Key Feature Sets
Unleash the ability to work with different types and shapes of data that serve your needs.
- 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.
- 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
- Challenger modes
- Humble AI
- Prediction intervals
The Only Scalable 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.
DataRobot not only helped us to reduce overhiring by 60%, but we were even able to increase sales by an unknown amount by rectifying underhiring, fulfilling more orders in our fulfillment centers.
DataRobot has helped our data science team to drastically accelerate our work. What would previously have taken us two-and-a-half weeks can now be done in hours. It’s like my group of 10 is really a group of 25, which would add substantially more costs for the same value.
The 10% increase in SKUs has had a substantial effect, and we plan to further optimize our supply chain and inventory management, resulting in savings of up to $200 million.