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Capability matrix

The following table provides an evolving comparison of capabilities available in DataRobot Classic and Workbench.

Feature DataRobot Classic Workbench
General platform features
Sharing Data, projects Data, Use Cases
Business-wide solution No, single projects Yes, experiments in a Use Case
Authentication SSO, 2FA, API key management SSO, 2FA, API key management
Data-related capabilities
Data sources Certified JDBC Connectors, local file upload, URL Snowflake, local file upload, static and snapshot datasets created in DataRobot Classic
Data preparation No Wrangling
Feature Discovery Yes No
Data Quality Assessment Yes No
Data storage AI Catalog Data Registry
Modeling-related capabilities
Modeling types Binary classification, regression, multiclass, multilabel, clustering, anomaly detection Binary classification, regression
Partitioning Random, Partition Feature, Group, Date/Time, Stratified Random, Stratified, Date/Time
TVH partitioning Yes Yes
Feature lists Automatic and manual Automatic (Informative and Raw, Univariate Selections, Reduced Features)
Modeling modes Quick, full Autopilot, Comprehensive, Manual Quick
Advanced options Yes Partitioning only
Time-aware Time series and OTV Yes
Blenders Yes, with option enabled No
Retraining Yes New feature list or sample size only
Model Repository Yes No
Composable ML Yes No
Visual AI Yes No
Bias and Fairness Yes No
Text AI Yes Yes, for supported model types
Location AI Yes No
Model insights See the full list Feature Impact, ROC Curve, Lift Chart, Residuals, Accuracy Over Time, Stability
Unlocking holdout Automatically for the recommended model or anything prepared for deployment Automatically for all models
Downloads Data, Leaderboard, Scoring Code, Compliance Report, exportable charts Compliance Report
Prediction-related capabilities
Predictions Predictions Model Overview > Make predictions
MLOps (coming soon)
MLOps Yes No
No-Code AI Apps
No-Code AI Apps Yes Yes
DataRobot Notebooks
DataRobot Notebooks Yes Yes

Updated May 25, 2023
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