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datarobot-model-deployment
Tools and guidance for deploying DataRobot models, managing deployments, configuring prediction environments, and deployment operations. Use when deploying models, creating or updating deployments, or configuring prediction environments.
Overview
Tools and guidance for deploying DataRobot models, managing deployments, configuring prediction environments, and deployment operations. Use when deploying models, creating or updating deployments, or configuring prediction environments.
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DataRobot Model Deployment Skill
This skill provides comprehensive guidance for deploying models, managing deployment configurations, and operating production deployments.
Quick Start
Most common use case: Deploy a trained model to production
- Get best model: Find the best model from a project (highest metric score)
- Create deployment:
create_deployment(model_id, deployment_name)to deploy model - Get endpoint:
get_deployment_endpoint(deployment_id)to retrieve prediction URL
Example: "Deploy the best model from project abc123 as 'Sales Prediction v1'"
When to use this skill
Use this skill when you need to:
- Deploy trained models to production
- Configure deployment settings and environments
- Manage multiple deployments
- Replace a deployment’s champion model with a new model version
- Configure prediction servers and environments
- Monitor deployment health and status
- Manage deployment access and permissions
Key capabilities
1. Deployment Creation
- Deploy models from projects or registered models
- Choose prediction environment (DataRobot Serverless, external)
- Configure deployment settings (challenger models, A/B testing)
- Set up deployment metadata and descriptions
2. Deployment Configuration
- Configure prediction servers and environments
- Set up batch prediction settings
- Configure real-time prediction endpoints
- Manage deployment credentials and access
3. Deployment Management
- Replace deployment champion model (model swap)
- Enable/disable deployments
- Manage challenger models for A/B testing
- Configure replacement policies
4. Deployment Operations
- Get deployment information and status
- Retrieve deployment endpoints
- Manage deployment settings
- Handle deployment errors and issues
Workflow examples
Example 1: Deploy a model to production
User request: "Deploy the best model from project abc123 to production with the name 'Sales Prediction v1'."
Agent workflow:
- Get the best model from the project (highest metric score)
- Create a new deployment with the model
- Configure deployment settings (name, description, environment)
- Set up prediction environment (DataRobot Serverless recommended)
- Retrieve deployment endpoint and credentials
- Verify deployment is active and ready for predictions
Example 2: Update deployment with new model
User request: "Replace the model in deployment xyz789 with the latest model from project abc123."
Agent workflow:
- Get the latest model from the project
- Retrieve current deployment information
- Validate the replacement model is eligible (
deployment.validate_replacement_model(...)) - Perform model replacement (
deployment.perform_model_replace(...)) - Verify replacement completed successfully
- Report deployment update status
Using DataRobot SDK
This skill guides you to use the DataRobot Python SDK directly. Install the SDK if needed:
pip install datarobot
Key SDK Operations
Use these DataRobot SDK methods for deployment management:
Deployments:
dr.Deployment.create_from_learning_model(model_id, label)- Create deploymentdr.Deployment.get(deployment_id)- Get deployment detailsdr.Deployment.list(project_id)- List deploymentsdeployment.delete()- Delete deployment
Model Replacement (champion swap):
deployment.validate_replacement_model(new_model_id=...)- Validate replacement eligibilitydeployment.perform_model_replace(new_model_id=..., reason=...)- Replace champion model (async)
Challenger Models (limited via SDK):
deployment.list_challengers()- List challenger models (if enabled/configured)deployment.get_challenger_models_settings()/deployment.update_challenger_models_settings(...)- Configure challenger models settings
Deployment Info:
deployment.get_features()- Get required features
See the Common Patterns section below for complete examples.
Best practices
- Naming conventions: Use clear, versioned names for deployments
- Environment selection: Choose appropriate prediction environment for your use case
- Challenger models: Use challenger models to test new models before full replacement
- Monitoring: Set up monitoring and alerts for production deployments
- Documentation: Document deployment purpose, model version, and configuration
- Access control: Configure appropriate access permissions for deployments
Common patterns
Pattern 1: Standard deployment
import datarobot as dr
# Initialize client
dr.Client()
# Get best model from project
models = dr.Model.list("abc123")
best_model = max(models, key=lambda m: m.metrics.get("AUC", 0))
# Create deployment
deployment = dr.Deployment.create_from_learning_model(
model_id=best_model.id,
label="Sales Prediction v1",
description="Production deployment for sales forecasting",
)
print(f"Deployment created: {deployment.id}")
Pattern 2: Deployment with challenger
import datarobot as dr
# Create deployment with primary model
deployment = dr.Deployment.create_from_learning_model(
model_id=primary_model.id, label="Sales Prediction v2"
)
# List challengers (if challenger models are configured/enabled)
challengers = deployment.list_challengers()
print(f"Challengers: {len(challengers)}")
Deployment environments
DataRobot Serverless
- Fully managed prediction environment
- Automatic scaling
- No infrastructure management
- Recommended for most use cases
External deployment
- Deploy to your own infrastructure
- More control over resources
- Requires infrastructure management
- Use for specific compliance or performance requirements
Deployment lifecycle
- Create: Deploy model to production environment
- Monitor: Track predictions, performance, and health
- Update: Replace with new model versions as needed
- Retire: Disable or archive old deployments
Error handling
Common errors and solutions:
- Model not found: Verify model ID and project access
- Deployment creation failures: Check prediction environment availability
- Endpoint access issues: Verify credentials and permissions
- Update failures: Ensure new model is compatible with deployment settings
SDK Setup
Install DataRobot SDK
pip install datarobot
Initialize Client
import datarobot as dr
dr.Client()
Resources
File metadata
name: datarobot-model-deployment description: Tools and guidance for deploying DataRobot models, managing deployments, configuring prediction environments, and deployment operations. Use when deploying models, creating or updating deployments, or configuring prediction environments.
View original text
---
name: datarobot-model-deployment
description: Tools and guidance for deploying DataRobot models, managing deployments, configuring prediction environments, and deployment operations. Use when deploying models, creating or updating deployments, or configuring prediction environments.
---
# DataRobot Model Deployment Skill
This skill provides comprehensive guidance for deploying models, managing deployment configurations, and operating production deployments.
## Quick Start
**Most common use case**: Deploy a trained model to production
1. **Get best model**: Find the best model from a project (highest metric score)
2. **Create deployment**: `create_deployment(model_id, deployment_name)` to deploy model
3. **Get endpoint**: `get_deployment_endpoint(deployment_id)` to retrieve prediction URL
**Example**: "Deploy the best model from project abc123 as 'Sales Prediction v1'"
## When to use this skill
Use this skill when you need to:
- Deploy trained models to production
- Configure deployment settings and environments
- Manage multiple deployments
- Replace a deployment’s champion model with a new model version
- Configure prediction servers and environments
- Monitor deployment health and status
- Manage deployment access and permissions
## Key capabilities
### 1. Deployment Creation
- Deploy models from projects or registered models
- Choose prediction environment (DataRobot Serverless, external)
- Configure deployment settings (challenger models, A/B testing)
- Set up deployment metadata and descriptions
### 2. Deployment Configuration
- Configure prediction servers and environments
- Set up batch prediction settings
- Configure real-time prediction endpoints
- Manage deployment credentials and access
### 3. Deployment Management
- Replace deployment champion model (model swap)
- Enable/disable deployments
- Manage challenger models for A/B testing
- Configure replacement policies
### 4. Deployment Operations
- Get deployment information and status
- Retrieve deployment endpoints
- Manage deployment settings
- Handle deployment errors and issues
## Workflow examples
### Example 1: Deploy a model to production
**User request**: "Deploy the best model from project abc123 to production with the name 'Sales Prediction v1'."
**Agent workflow**:
1. Get the best model from the project (highest metric score)
2. Create a new deployment with the model
3. Configure deployment settings (name, description, environment)
4. Set up prediction environment (DataRobot Serverless recommended)
5. Retrieve deployment endpoint and credentials
6. Verify deployment is active and ready for predictions
### Example 2: Update deployment with new model
**User request**: "Replace the model in deployment xyz789 with the latest model from project abc123."
**Agent workflow**:
1. Get the latest model from the project
2. Retrieve current deployment information
3. Validate the replacement model is eligible (`deployment.validate_replacement_model(...)`)
4. Perform model replacement (`deployment.perform_model_replace(...)`)
5. Verify replacement completed successfully
6. Report deployment update status
## Using DataRobot SDK
This skill guides you to use the DataRobot Python SDK directly. Install the SDK if needed:
```bash
pip install datarobot
```
### Key SDK Operations
Use these DataRobot SDK methods for deployment management:
**Deployments**:
- `dr.Deployment.create_from_learning_model(model_id, label)` - Create deployment
- `dr.Deployment.get(deployment_id)` - Get deployment details
- `dr.Deployment.list(project_id)` - List deployments
- `deployment.delete()` - Delete deployment
**Model Replacement (champion swap)**:
- `deployment.validate_replacement_model(new_model_id=...)` - Validate replacement eligibility
- `deployment.perform_model_replace(new_model_id=..., reason=...)` - Replace champion model (async)
**Challenger Models (limited via SDK)**:
- `deployment.list_challengers()` - List challenger models (if enabled/configured)
- `deployment.get_challenger_models_settings()` / `deployment.update_challenger_models_settings(...)` - Configure challenger models settings
**Deployment Info**:
- `deployment.get_features()` - Get required features
See the [Common Patterns](#common-patterns) section below for complete examples.
## Best practices
1. **Naming conventions**: Use clear, versioned names for deployments
2. **Environment selection**: Choose appropriate prediction environment for your use case
3. **Challenger models**: Use challenger models to test new models before full replacement
4. **Monitoring**: Set up monitoring and alerts for production deployments
5. **Documentation**: Document deployment purpose, model version, and configuration
6. **Access control**: Configure appropriate access permissions for deployments
## Common patterns
### Pattern 1: Standard deployment
```python
import datarobot as dr
# Initialize client
dr.Client()
# Get best model from project
models = dr.Model.list("abc123")
best_model = max(models, key=lambda m: m.metrics.get("AUC", 0))
# Create deployment
deployment = dr.Deployment.create_from_learning_model(
model_id=best_model.id,
label="Sales Prediction v1",
description="Production deployment for sales forecasting",
)
print(f"Deployment created: {deployment.id}")
```
### Pattern 2: Deployment with challenger
```python
import datarobot as dr
# Create deployment with primary model
deployment = dr.Deployment.create_from_learning_model(
model_id=primary_model.id, label="Sales Prediction v2"
)
# List challengers (if challenger models are configured/enabled)
challengers = deployment.list_challengers()
print(f"Challengers: {len(challengers)}")
```
## Deployment environments
### DataRobot Serverless
- Fully managed prediction environment
- Automatic scaling
- No infrastructure management
- Recommended for most use cases
### External deployment
- Deploy to your own infrastructure
- More control over resources
- Requires infrastructure management
- Use for specific compliance or performance requirements
## Deployment lifecycle
1. **Create**: Deploy model to production environment
2. **Monitor**: Track predictions, performance, and health
3. **Update**: Replace with new model versions as needed
4. **Retire**: Disable or archive old deployments
## Error handling
Common errors and solutions:
- **Model not found**: Verify model ID and project access
- **Deployment creation failures**: Check prediction environment availability
- **Endpoint access issues**: Verify credentials and permissions
- **Update failures**: Ensure new model is compatible with deployment settings
## SDK Setup
### Install DataRobot SDK
```bash
pip install datarobot
```
### Initialize Client
```python
import datarobot as dr
dr.Client()
```
## Resources
- [DataRobot Python SDK Documentation](https://datarobot-public-api-client.readthedocs-hosted.com/)
- [DataRobot Deployment Documentation](https://docs.datarobot.com/en/docs/mlops/deployment/deploy-methods/add-deploy-info.html)
- [Prediction Environments Guide](https://docs.datarobot.com/en/docs/mlops/deployment/prediction-env/pred-env-deploy.html)
- [Challenger Models Documentation](https://docs.datarobot.com/en/docs/mlops/monitor/challengers.html)
Review the source
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- Get the skill
- Price unconfirmed
- Run it
- Requirements have not been confirmed. Check the source for agent, API and service charges.
- License
- Apache-2.0
- Price unconfirmed
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Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: Apache-2.0
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
- Low GitHub adoption signal
- AI review approval is missing
- This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 25 GitHub stars
- Stars/forks activity: 25 stars, 23 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
IndexedStatic Checked
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- datarobot-oss/datarobot-agent-skills
- License
- Apache-2.0
- Version
- Unknown
- Last GitHub push
- Sep 12, 2026
- Registry updated
- Sep 12, 2026
- Instruction path
- skills/datarobot-model-deployment/SKILL.md @ e6dddbe327dc
Version reported in registry metadata; check source releases before relying on it.
Quality
55/100
Promising
Trust
60/100
Sandbox only
Audit
71/100
Risky
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
- Low GitHub adoption signal
- AI review approval is missing
- This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 25 GitHub stars
- Stars/forks activity: 25 stars, 23 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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"maintenance": "21d since push",
"risk": "Risky"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"Low GitHub adoption signal",
"Audit risk risky exceeds max_risk=medium",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required"
],
"agent_contract": {
"task_input": "Use datarobot-model-deployment in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 68/100 Manual review",
"Audit: 71/100 Risky",
"Safety: 31/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "datarobot-oss-datarobot-model-deployment (datarobot-model-deployment)",
"install_command": "npx skills add datarobot-oss/datarobot-agent-skills --skill datarobot-model-deployment",
"risk_summary": "Risky; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "datarobot-oss-datarobot-model-deployment",
"task": "Use datarobot-model-deployment in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/datarobot-oss-datarobot-model-deployment",
"api": "https://www.openagentskill.com/api/agent/skills/datarobot-oss-datarobot-model-deployment",
"audit": "https://www.openagentskill.com/skills/datarobot-oss-datarobot-model-deployment/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=datarobot-oss-datarobot-model-deployment&task=Use%20datarobot-model-deployment%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20datarobot-model-deployment%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20datarobot-model-deployment%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/datarobot-oss-datarobot-model-deployment/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/datarobot-oss-datarobot-model-deployment"
}
}For the creator
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- datarobot-oss
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[](https://www.openagentskill.com/skills/datarobot-oss-datarobot-model-deployment/audit)
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