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Composable ML considerations

Consider the following when working with Composable ML.

Environment support

Composable ML is supported on DataRobot’s Managed AI Cloud (US, EU).

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 / 4GB 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:

  • AutoML, including OTV (not time series) and Feature Discovery.

  • Estimators, both built-in and custom, are available for binary classification, regression, multiclass, and anomaly detection.

  • Preprocessing, both built-in and custom.

API

  • Python SDK 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:

    • Built-in metrics.
    • Custom loss functions (as a part of your custom estimator)
    • Custom metrics are not yet supported.
  • Exposure, Weight, Counts of Events, Offset

    • For custom estimators, weight is supported (others are not).
    • For built-in estimators, all options are supported.

    Note

    DataRobot 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 use y or y/exposure as 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 built-in tasks, but is not yet supported for custom tasks (although it can be embedded inside a task).

  • Blenders are supported with the exception of custom estimators. You can however manually create a blueprint that uses multiple estimators.

Insights and compliance documentation

  • Model-agnostic insights are supported:

    • All model evaluation insights are supported (including Evaluate tab, Model Comparison, Learning Curve, Speed vs Accuracy, etc.).
    • Permutation-based Feature Impact, Feature Effects, and XEMP Prediction Explanations are supported (including for Anomaly Detection models).
  • Model-specific insights offer limited support:

    • SHAP-based Feature Impact, Prediction Explanations, Hotspots, and Variable Effects are not supported.
    • Coefficients and Tree-based variable importance are supported for built-in estimators. Word Cloud is supported for built-in estimators when the parameter WordCloud is set to True.
    • Rating tables are not supported when there are custom tasks.
  • 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.


Updated April 27, 2022
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