MLOps for IT Leaders

Start managing and governing your AI by bridging the gap between IT and data science teams.

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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.

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

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:

Understand the True Impact of DataRobot

Looking closely at IT Costs, maintenance and ROI, learn firsthand how customers are receiving ROI of 514% with payback in less than three months in the commissioned report, The Total Economic Impact™ of DataRobot.

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DataRobot The Total Economic Impact TM of DataRobot Resource card v1.0

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
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.

– Customer, Manager of Data Science, Experimentation, and Research, eCommerce, Retail
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.

– Customer, Head of Data Science, Healthcare
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.

– Customer, Vice president of Advanced Analytics and Data Engineering, Manufacturing