DataRobot - OSS
Official@datarobot-oss · United States of America
DataRobot Open Source
Agent Skills by DataRobot - OSS
Showing 12 vetted skills indexed across 1 GitHub repositories.
datarobot-ossdatarobot-agent-assist
Design, simulate, and deploy DataRobot AI agents from an agent_spec.md workflow.
datarobot-ossdatarobot-model-deployment
Automate DataRobot model deployment to production endpoints with the Python SDK.
datarobot-ossdatarobot-model-training
Automate DataRobot model training from project setup to model selection.
datarobot-ossdatarobot-model-explainability
Compute SHAP matrices and XEMP prediction explanations for DataRobot models.
datarobot-ossdatarobot-predictions
Generate and score prediction inputs for DataRobot deployments with optional SHAP or XEMP explanations.
datarobot-ossdatarobot-model-monitoring
Monitor DataRobot deployments for drift, anomalies, and model health.
datarobot-ossdatarobot-external-agent-monitoring
Instrument external AI agents to export OpenTelemetry traces, logs, and metrics into DataRobot.
datarobot-ossdatarobot-feature-engineering
Discover DataRobot-derived features and interpret feature impact scores.
datarobot-ossdatarobot-app-framework-cicd
Automate CI/CD setup for DataRobot application templates with Pulumi.
datarobot-ossdatarobot-data-preparation
Validate and upload CSV or Parquet datasets via the DataRobot Python SDK.
datarobot-ossdatarobot-setup
Install DataRobot CLIs, SDKs, and infrastructure tooling for local enterprise AI development.
datarobot-ossprogressive-disclosure
Refactors large Skill instructions into linked reference files, preserving meaning and guardrails.
Frequently Asked Questions About DataRobot - OSS
FAQPage SchemaWhat specific tasks can be performed using these capabilities?▼
These capabilities enable end-to-end model lifecycle management, including dataset validation, training, deployment, and performance monitoring. Users can generate SHAP or XEMP explanations for predictions, detect drift in production environments, and instrument external systems to export telemetry data for comprehensive health analysis.
Which personas benefit most from these technical resources?▼
Data scientists, machine learning engineers, and MLOps practitioners are the primary target personas. These resources are designed for technical teams responsible for maintaining model health, ensuring interpretability in predictive outputs, and integrating complex model deployments into existing enterprise infrastructure.
What are the prerequisites for implementing these model monitoring features?▼
Implementation requires an active DataRobot environment and access to the relevant Python-based interface. Users must ensure their datasets are formatted as CSV or Parquet files and that their infrastructure supports OpenTelemetry standards for exporting logs and metrics from external systems into the monitoring platform.