MLOps for IT Leaders
Start managing and governing your AI by bridging the gap between IT and data science teams.
The Advantage of MLOps for IT Leaders
CIOs, CTOs and IT leaders are accustomed to taking ownership of new technologies and business services. However, machine learning applications are very different from anything they have previously owned due to the unique sensitivities and intricacies of machine learning models in production and their probabilistic and unpredictable nature. The only way for them to agree to take ownership of machine learning in production is to empower their teams with advanced management systems that are designed for managing machine learning by Operations professionals who are not machine learning-savvy. This is MLOps.
Enter MLOps, a solution that makes IT Operations and the lives of IT leaders easier by creating a centralized hub for deployment, monitoring, and production of AI and machine learning.
Enter MLOps, a solution that makes IT Operations and the lives of IT leaders easier by creating a centralized hub for deployment, monitoring, and production of AI and machine learning.
Deployment
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.
Monitoring
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.
Lifecycle Management
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.
Model Governance
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.
See What MLOps Can Do for IT Leaders and Teams
DataRobot MLOps allows IT Teams to manage cutting edge predictive models in an efficient and value-driven way.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
- Challenger modes
- Humble AI
- Prediction intervals
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:
Take the next step to managing and governing your AI.