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.
MLOps makes model deployment easy. Operations teams, not data scientists, can deploy models written in a variety of modern programming languages like Python and R onto modern runtime environments in the cloud or on-premise. Users of the MLOps system don’t have to know any of these technologies to drag and drop a model into the system, create a container, and deploy the model to a production environment.
MLOps gives you monitoring that is designed for machine learning. 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. With MLOps monitoring in place, your teams can deploy and manage thousands of models, and your business will be ready to scale production AI.
Models need to be updated frequently and seamlessly. MLOps 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, failover procedures, and full version control for simple rollback to prior model versions.
MLOps governance provides the integrations and capabilities you need to ensure consistent, repeatable, and reportable processes for your 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.
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
- Champion/Challenger gates into production
- Humble AI – built in mechanisms ensuring trust in your models
- Prediction intervals
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.
Take an MLOps Course
Start your journey to becoming certified in MLOps by taking a self-starter or instructor-led course.
Join the Community
Discuss MLOps and other AI-related topics with other data engineers in the DataRobot Community.
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