# Composable ML considerations
URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-consider.html

> Platform support and considerations for working with Composable ML.

# Composable ML considerations

Consider the following when working with Composable ML.

### Environment support

Custom tasks are supported on DataRobot’s managed AI Platform (US, EU). Composable ML blueprints with DataRobot tasks are available for all users.

#### DRUM supported OSs

- DRUM works on Mac OS, Linux, and Windows 10. Testing is on Linux only and therefore compatibility issues may occur when running on other platforms.

#### Custom task languages

- Python and R are supported.
- SAS is not supported.

#### Task limits

| Component | Size |
| --- | --- |
| RAM | 60GB training / 14GB scoring |
| CPU Cores | 4 |
| GPU | not supported |
| Storage | 350GB |
| Artifact (Serialized trained model/transformer size) | 10GB |
| Timeout | 72 hrs (for fit) |
| Max custom tasks per blueprint | 3 |

### Modeling support

The following describes modeling-specific capabilities.

#### Modeling specifics

The following are supported:

- Predictive ML, including time-aware but not time series, and Feature Discovery.
- Estimators, both built-in and custom, are available for binary classification, regression, multiclass, anomaly detection, and clustering.
- Preprocessing, both built-in and custom.

#### API

- Python API client for blueprint generation.
- Python client for custom task generation.

#### Modeling options

The following describes support for advanced modeling options with custom blueprints:

- Metrics and loss function, where “loss function” is used to train a model and “metric” is used to evaluate models and for accuracy monitoring. Typically, the same measure is used for both. Support includes:
- Exposure, Weight, Counts of Events, Offset NoteDataRobot does not indicate whether a task takes into account any of these options. As a result, using Composable ML on a project that uses those options is not recommended. (For example, if you train a custom blueprint on a project that uses exposure, there is no guarantee whether the model will useyory/exposureas the target.) On the other hand, when using blueprints generated by Autopilot, these options are taken into account correctly, in line with the project settings.
- Monotonic constraints are not supported.
- Hyperparameter tuning is supported for custom and built-in tasks.
- Blenders are supported with the exception of custom estimators. You can howevermanuallycreate a blueprint that uses multiple estimators.

#### Insights and compliance documentation

- Model-agnosticinsights are supported:
- Model-specificinsights offer limited support:
- Compliance documentation is supported.

### Deployment options

- All MLOps model monitoring and management features are supported.
- Deployment of a custom blueprint inside the DataRobot platform is supported.
- Deployment outside of the DataRobot platform (using Scoring Code or Portable Prediction Server) is only supported for blueprints without custom tasks.

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