seldon-model-deployer

Seldon Core deployment skill for model serving, A/B testing, and canary deployments on Kubernetes.

509 stars

Best use case

seldon-model-deployer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Seldon Core deployment skill for model serving, A/B testing, and canary deployments on Kubernetes.

Teams using seldon-model-deployer should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/seldon-model-deployer/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/data-science-ml/skills/seldon-model-deployer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/seldon-model-deployer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How seldon-model-deployer Compares

Feature / Agentseldon-model-deployerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Seldon Core deployment skill for model serving, A/B testing, and canary deployments on Kubernetes.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# seldon-model-deployer

## Overview

Seldon Core deployment skill for model serving, A/B testing, canary deployments, and advanced inference graphs on Kubernetes.

## Capabilities

- SeldonDeployment creation and management
- Multi-model serving
- Traffic splitting (canary/shadow/A/B)
- Model monitoring integration
- Custom inference graphs
- Explainer deployment (SHAP, Anchor)
- Request logging and tracing
- Autoscaling configuration

## Target Processes

- Model Deployment Pipeline with Canary Release
- A/B Testing Framework for ML Models
- ML Model Retraining Pipeline

## Tools and Libraries

- Seldon Core
- Seldon Deploy
- Kubernetes
- Istio/Ambassador (ingress)

## Input Schema

```json
{
  "type": "object",
  "required": ["action"],
  "properties": {
    "action": {
      "type": "string",
      "enum": ["deploy", "update", "rollback", "delete", "status", "traffic-split"],
      "description": "Seldon action to perform"
    },
    "deploymentConfig": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "namespace": { "type": "string" },
        "modelUri": { "type": "string" },
        "implementation": { "type": "string" },
        "replicas": { "type": "integer" },
        "resources": {
          "type": "object",
          "properties": {
            "requests": { "type": "object" },
            "limits": { "type": "object" }
          }
        }
      }
    },
    "trafficConfig": {
      "type": "object",
      "properties": {
        "canaryPercent": { "type": "integer" },
        "shadowEnabled": { "type": "boolean" },
        "abTestEnabled": { "type": "boolean" }
      }
    },
    "explainerConfig": {
      "type": "object",
      "properties": {
        "type": { "type": "string", "enum": ["anchor_tabular", "anchor_text", "shap"] },
        "enabled": { "type": "boolean" }
      }
    }
  }
}
```

## Output Schema

```json
{
  "type": "object",
  "required": ["status", "action"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error", "pending"]
    },
    "action": {
      "type": "string"
    },
    "deploymentName": {
      "type": "string"
    },
    "endpoint": {
      "type": "string"
    },
    "deploymentStatus": {
      "type": "string",
      "enum": ["creating", "available", "failed", "unknown"]
    },
    "replicas": {
      "type": "object",
      "properties": {
        "desired": { "type": "integer" },
        "ready": { "type": "integer" }
      }
    },
    "trafficSplit": {
      "type": "object"
    }
  }
}
```

## Usage Example

```javascript
{
  kind: 'skill',
  title: 'Deploy model with canary',
  skill: {
    name: 'seldon-model-deployer',
    context: {
      action: 'deploy',
      deploymentConfig: {
        name: 'fraud-detector',
        namespace: 'ml-serving',
        modelUri: 'gs://models/fraud-v2',
        implementation: 'SKLEARN_SERVER',
        replicas: 3
      },
      trafficConfig: {
        canaryPercent: 10
      },
      explainerConfig: {
        type: 'shap',
        enabled: true
      }
    }
  }
}
```

Related Skills

model

509
from a5c-ai/babysitter

Inspect or change Babysitter model-routing policy by phase.

threat-modeler

509
from a5c-ai/babysitter

Generate threat models using STRIDE, PASTA, or VAST methodologies

swagger-ui-deployer

509
from a5c-ai/babysitter

Deploy interactive API documentation using Swagger UI with custom branding

urdf-sdf-model

509
from a5c-ai/babysitter

Expert skill for robot model creation and validation in URDF and SDF formats. Generate URDF files with proper link-joint hierarchy, create Xacro macros, calculate inertial properties, configure joint types, and validate models.

topic-modeling-text-mining

509
from a5c-ai/babysitter

Apply LDA, NMF, and other computational methods to discover patterns in large text corpora with appropriate parameter tuning

systems-dynamics-modeler

509
from a5c-ai/babysitter

Skill for building and simulating systems dynamics models

noise-modeler

509
from a5c-ai/babysitter

Quantum noise modeling skill for simulation and hardware characterization

pymc-bayesian-modeler

509
from a5c-ai/babysitter

PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis

comsol-multiphysics-modeler

509
from a5c-ai/babysitter

COMSOL finite element skill for multiphysics simulations including electromagnetics, heat transfer, and fluid dynamics

environmental-fate-modeler

509
from a5c-ai/babysitter

Environmental nanosafety skill for modeling nanomaterial environmental fate and transport

cad-modeling

509
from a5c-ai/babysitter

Expert skill for parametric 3D CAD model development with design intent and configuration management

stan-bayesian-modeling

509
from a5c-ai/babysitter

Stan probabilistic programming for Bayesian inference