Data Science Leaders

MLOps for Data Science Leaders

Meet the only model governance solution you will need.

The Advantage of MLOps for the Data Science Leader

Data Science leaders need a way to demonstrate value to everyone, not just the data science team. With many processes and people involved, just getting a model off the ground and into production can be one of the data science team’s hardest tasks.

Enter MLOps, a solution that brings together data science, engineering, DevOps, and ITOps. Getting models into production and bridging the gap between stakeholder teams has never been easier. DataRobot’s MLOps solution lets your team get back to solving the problems of data science, while letting others rest easy that models are trustworthy, unbiased, and easy to manage.

MLOps outlined
Deployment
MLOps offers flexibility by allowing you to deploy using a variety of ML platforms, languages, and frameworks. This way your team decides on the best approach to the problem and MLOps just deploys it.
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Monitoring
Monitoring models is essential to ensuring 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.
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Production Lifecycle Management
Models need to be updated. Manual updates are time consuming and problematic. Lifecycle management is key for ensuring a team can manage a large portfolio of models.
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Production Model Governance
It’s not just about deploying models, it’s about having a robust governance practices and tools to minimize risk and ensure regulatory compliance.

See What MLOps Can Do for Data Science Leaders

DataRobot MLOps allows data science leaders and teams to embed cutting edge predictive models in an efficient and value-driven way. From deploying agents to being cloud agnostic, MLOps is the flexible tool you need to run your models.

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 ML 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
Agent based

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.

MLOps Customers

Companies across every industry leverage DataRobot’s MLOps solution, such as:
PNC
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EmpiricHealthLogo Line
Orix
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Understand the True Impact of DataRobot on Data Science

From challenges in data science, reducing costs, and improving operational efficiencies, 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 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

    Take the next step to managing and governing your AI.