MLOps offers flexibility by allowing you to deploy using a variety of ML platforms, languages, and frameworks. This way your team decides on the best approach to the problem and MLOps just deploys it.
Monitoring models is essential to ensuring they are continually producing value. MLOps gives you a system for monitoring all your models, no matter where they are deployed or what frameworks you used to build the models.
Models need to be updated. Manual updates are time consuming and problematic. Lifecycle management is key for ensuring a team can manage a large portfolio of models.
It’s not just about deploying models, it’s about having a robust governance practices and tools to minimize risk and ensure regulatory compliance.
DataRobot MLOps allows data science leaders and teams to embed cutting edge predictive models in an efficient and value-driven way. From deploying agents to being cloud agnostic, MLOps is the flexible tool you need to run your models.
See What MLOps Can Do for Data Science Leaders
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 ML 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
The Only Scalable MLOps Architecture
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.