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.
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.
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.
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.
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.
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
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:
Related resources
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EbookDataRobot MLOps Agents: Provide Centralized Monitoring for All Your Production ModelsDownload this ebook to learn more about DataRobot MLOps monitoring agents and take your first step to creating a center of excellence for your production AI.Read More -
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White PaperMLOps 101: The Foundation for Your AI StrategyMachine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability.Read More -
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On-Demand WebinarAccelerating AI Adoption with Machine Learning Operations (MLOps)In this webinar, hosted by Data Science Central, you’ll get an overview of MLOps and discover the issues, capabilities, and best practices required for successful and sustained deployment of machine learning in production.Watch the Webinar -
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Data SheetDataRobot MLOpsAI and machine learning projects should be driving the future of your business. Many companies, however, can’t take full advantage of data science investments. These organizations lack the skills and resources to deploy, manage, and govern machine earning in production. Moreover, the results of attempting to use AI models without proper monitoring and controls can be catastrophic, including lost revenues and the trust of business executives, investors, and customers.Download the PDF -
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BlogAnnouncing DataRobot MLOpsRead More
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![DataRobot MLOps Agents Provide Centralized Monitoring for All Your Production Models hero banner]()
EbookDataRobot MLOps Agents: Provide Centralized Monitoring for All Your Production ModelsDownload this ebook to learn more about DataRobot MLOps monitoring agents and take your first step to creating a center of excellence for your production AI.Read More -
![MLOps 101 The Foundation for Your AI Strategy hero banner]()
White PaperMLOps 101: The Foundation for Your AI StrategyMachine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability.Read More -
![Accelerating AI Adoption with Machine Learning Operations MLOps hero banner]()
On-Demand WebinarAccelerating AI Adoption with Machine Learning Operations (MLOps)In this webinar, hosted by Data Science Central, you’ll get an overview of MLOps and discover the issues, capabilities, and best practices required for successful and sustained deployment of machine learning in production.Watch the Webinar -
![]()
Data SheetDataRobot MLOpsAI and machine learning projects should be driving the future of your business. Many companies, however, can’t take full advantage of data science investments. These organizations lack the skills and resources to deploy, manage, and govern machine earning in production. Moreover, the results of attempting to use AI models without proper monitoring and controls can be catastrophic, including lost revenues and the trust of business executives, investors, and customers.Download the PDF -
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BlogAnnouncing DataRobot MLOpsRead More
