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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.

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Prix non confirmé★ 25 Stars GitHubRegistre mis à jour · 12 sept. 2026agent-skill

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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 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 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

Voir la source ↗

Métadonnées du fichier

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.

Voir le texte original

---
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)

Examiner la source

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Obtenir le skill: Prix non confirmé
L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: Apache-2.0
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: 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
L’approbation de revue IA est absente
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

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

RépertoriéContrôle statique

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: datarobot-oss/datarobot-agent-skills
Licence: Apache-2.0
Version: Unknown
Dernier push GitHub: 12 sept. 2026
Registre mis à jour: 12 sept. 2026
Chemin des instructions: skills/datarobot-model-deployment/SKILL.md @ e6dddbe327dc

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

52/100

Revue nécessaire

Confiance

59/100

Do not auto-install

Audit

68/100

Risqué

Ouvrir l’audit complet Voir le rapport d’évaluation

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
L’approbation de revue IA est absente
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: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

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      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 68,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "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"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 52,
    "label": "Needs review"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "1mo 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: 67/100 Manual review",
      "Audit: 68/100 Risky",
      "Safety: 28/100 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"
  }
}

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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: datarobot-oss
Source: datarobot-oss/datarobot-agent-skills
Indexé par: Index communautaire OpenAgentSkill

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