MLOps for Data Engineers

Your centralized hub for сomplete lifecycle management of machine learning models.

The Advantage of MLOps for Data Engineers

Data engineers are often tasked with blazing the trail to productionalizing machine learning for the whole organization, mainly based on their engineering background and familiarity with the intricacies and the behavior of data. This often leaves them with a very difficult task at hand.

Enter MLOps, a solution that proactively monitors and manages the lifecycle of machine learning models. With MLOps, data engineers can update, test, and validate deployments from a centralized hub no matter what types of models they are running.

Deployment

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.

Monitoring

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.

Lifecycle Management

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.

Model Governance

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.

See What MLOps Can Do for Data Engineers

DataRobot MLOps allows data engineers 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
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 help your team scale, getting 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:

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.

Clayton Howard
Director of Analytics, Net Pay Advance
With MLOps, we were able to deploy both DataRobot and non-DataRobot models within minutes rather than weeks, enabling us to achieve a far faster time to value than with homegrown deployments. In addition, the monitoring capabilities ensure that our models are generalizing appropriately to new data. We have so far had 100% uptime on our deployments.

Derek Schaff
VP of Data Science, Clear Spring Property and Casualty Company