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Automate algorithm selection, hyperparameter tuning, and model validation with minimal coding.
Track model drift, accuracy, and performance in real time with alerts and retraining triggers.
Deploy models via REST APIs, batch scoring, or integrations with cloud and on-prem systems.
Understand model decisions with feature impact scores, prediction explanations, and fairness metrics.
Clean and transform datasets using built-in preprocessing tools or external pipelines.
Let DataRobot build, rank, and validate multiple models using its automated engine.
Choose the best-performing model and deploy it via API or batch scoring.
Track performance, detect drift, and retrain models as needed to maintain accuracy.
Embed predictions into dashboards, apps, or workflows using BI tools or custom apps.
# DataRobot Python Prediction Example
import datarobot as dr
deployment = dr.Deployment.get(deployment_id='123456')
prediction = deployment.predict({'Age': 45, 'Income': 75000, 'Region': 'North'})
print(prediction)Identify customers likely to leave and trigger retention workflows.
Predict future revenue using historical trends and external factors.
Detect anomalies in transactions using classification models.
Predict patient outcomes and optimize treatment plans.
Explore DataRobot’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about DataRobot’s capabilities, usage, and ecosystem.