MLOps for Data Scientists
Meet the only model governance solution you will need.
The Advantage of MLOps for Data Scientists
With numerous processes and teams involved in getting models into production, many data scientists find that their models get stuck at the finish line.
Enter MLOps, a solution that provides data scientists with an easier, more efficient way to deploy, maintain, monitor, and update models. Start getting models into production and bridging the gap between stakeholder teams so that you can focus on data science.
Deployment
MLOps offers deployment that is totally agnostic. You pick which platform you want to deploy on. You pick which frameworks or languages you want to use.
Monitoring
Monitoring models is essential to ensuring that 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.
Production Lifecycle Management
Your models will need to be updated. Manual updates are time-consuming and problematic. Lifecycle management makes it easier for data scientists to manage a large portfolio of production models.
Production Model Governance
Deployment is just the start. It’s also important to have in place robust governance practices, review processes, and tools to minimize risk and ensure regulatory compliance.
See What MLOps Can Do for Data Scientists
DataRobot MLOps allows data science leaders and teams to embed cutting edge predictive models in an efficient and value-driven way no matter what. From agents to being cloud agnostic, MLOps is flexible.
Three Key Feature Sets
Serving Predictions
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
- Alerts
- Audit logs
- Versioning and lineage
- Change approval workflows
- No-code prediction GUI
- Value and use case tracking
- RBAC
- 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
Agents
The Only Scalable MLOps Architecture
Monitoring agents can get you to the scale of putting thousands and hundreds of thousands of models into production. Regardless of where your model is built — cloud, Spark, Azure, servers — you will be able to access your models from one central hub. Use what you have today and manage in one view.
Learn More About MLOps
Access the following resources to strengthen your skills and understanding of MLOps.
MLOps Customers
Companies across every industry leverage DataRobot’s MLOps solution, such as:
Related resources
