4.MLOps for Renato 1

AI and ML Centers of Excellence

Create a single, normalized, and centralized solution for managing all machine learning models, regardless of how and where they were created, and regardless where they need to be deployed across the business.

The Advantage of Centralizing All Management of Production ML

Ensure all machine learning models and applications are managed and governed via a single controlled process across all your business units in order to avoid the risk of machine learning being loosely managed introducing endless risk and possible AI and production chaos.

MLOps can help AI and MLOps teams in these four areas:

MLOps outlined
Streamlining deployment into production from any machine learning Platform written in any language.
monitor search find detect risk error fraud dark
Monitoring designed and built from the ground up for the unique characteristics and sensitivities of machine learning models.
gear process create done check
Lifecycle Management
Easy access for Data Scientists and MLOps and Operations professionals to be notified and take action to ensure that models are continuously delivering expected results throughout their lifecycle.
model algorythm
Model Governance
Fully governed environments, maintaining full lineage and ensuring compliance and reducing risk from the whole process of managing machine learning in production.

See What MLOps Can Do for AI and Machine Learning Teams

DataRobot MLOps allows AI and MLOps teams to embed cutting edge predictive models in an efficient and value-driven way.

Three Key Feature Sets

Serving Predictions

Unleash the ability to work and experiment with different types of models created on any platform and in any language inside a single MLOps solution.

  • 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, regardless of run-time environment.

  • 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
Agent based


Unique approach to environment-agnostic MLOps Architecture

Monitoring agents can help you scale to thousands and hundreds of thousands of models in production, regardless of where they are running whether in the cloud or on-premise.

egardless of where your model is built — cloud, Spark, Azure, or servers — actively monitor and manage your models from one central hub. Without changing what you already have running, MLOps gives you a turnkey way to manage it all in one view.

MLOps Customers

Companies across every industry leverage DataRobot’s MLOps solution, such as:

PNC logo color
jmdc logo color
Liverpool Victoria logo color
first national bank fnb logo color
EmpiricHealthLogo Line
orix logo color
societe generale logo color
decode logo color
scout24 logo color
NTUC Income logo color
clear spring insurance logo color

Carbon Transforms Consumer Lending with DataRobot

Today, Carbon processes 150,000 loan applications each month through DataRobot’s prediction API and tracks those deployments in DataRobot MLOps.
DataRobot Carbon Case Study REsource Card v.1.0
  • 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

  • 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
    Derek Schaff

    VP of Data Science, Clear Spring Property and Casualty Company

    Take the next step to managing and governing your AI.