# MLOps
URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/index.html

> DataRobot machine learning operations (MLOps) provides a central hub for you to deploy, monitor, manage, and govern your models in production.

# MLOps

DataRobot MLOps provides a central hub to [deploy](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/index.html), [monitor](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/index.html), [manage](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/index.html), and [govern](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/index.html) all your models in production, regardless of how they were created or when and where they were deployed. MLOps helps improve and maintain the quality of your models using health monitoring that accommodates changing conditions via continuous, automated model competitions ( [challenger models](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html)). It also ensures that all centralized production machine learning processes work under a robust governance framework across your organization, leveraging and sharing the burden of production model management.

With MLOps, you can deploy any model to your production environment of choice. By instrumenting the [MLOps agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html), you can monitor any existing production model already deployed for live updates on behavior and performance from a single and centralized machine learning operations system. MLOps makes it easy to deploy models written in any open-source language or library and expose a production-quality, REST API to support real-time or batch predictions. MLOps also offers built-in [write-back integrations](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html) to systems such as Snowflake and Synapse.

MLOps provides constant monitoring and production diagnostics to improve the performance of your existing models. Automated best practices enable you to track [service health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html), [accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html), and [data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html) to explain why your model is degrading. You can build your own challenger models or use Automated Machine Learning to build them for you and test them against your current champion model. This process of continuous learning and evaluation enables you to avoid surprise changes in model performance.

The tools and capabilities of every deployment are determined by the data available to it: [training data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#training-data), [prediction data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#prediction-data), and outcome data (also referred to as [actuals](https://docs.datarobot.com/en/docs/reference/glossary/index.html#actuals)).

| Topic | Description |
| --- | --- |
| Deployment | How to bring models to production by following the workflows provided for all kinds of starting artifacts. |
| Deployment settings | How to use the settings tabs for individual MLOps features to add or update deployment functionality. |
| Lifecycle management | Maintaining model health to minimize inaccurate data, poor performance, or unexpected results from models in production. |
| Performance monitoring | Tracking the performance of models to identify potential issues, such as service errors or model accuracy decay, as soon as possible. |
| Governance | Enacting workflow requirements to ensure quality and comply with regulatory obligations. |
| MLOps FAQ | A list of frequently asked MLOps questions with brief answers linking to the relevant documentation. |

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