← Back to all products

Model Serving Templates

$49

FastAPI/Flask model serving endpoints, batched inference, A/B testing, and canary deployment configurations.

📁 8 files🏷 v1.0.0
MarkdownYAMLJSONDockerKubernetesFastAPIFlask

📁 File Structure 8 files

model-serving-templates/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-ab-testing-serving.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

Model Serving Templates

Production-ready templates for serving ML models via REST APIs. Includes FastAPI and Flask serving patterns, batched inference, A/B testing infrastructure, and canary deployment configurations.

What's Included

  • FastAPI model serving with async inference endpoints
  • Flask model serving with Gunicorn for legacy compatibility
  • Batched inference patterns for throughput optimization
  • A/B testing framework with traffic splitting
  • Canary deployment configs for Kubernetes
  • Request/response validation with Pydantic
  • Model loading with caching and warm-up

Quick Start


# 1. Copy the example config
cp config.example.yaml config.yaml

# 2. Install dependencies
pip install -r requirements.txt

# 3. Start the FastAPI serving endpoint
uvicorn serve:app --host 0.0.0.0 --port 8000

Prerequisites

  • Python 3.9+
  • FastAPI 0.100+ or Flask 2.x
  • Docker (for containerized deployment)
  • Kubernetes (optional, for canary deployments)

Contents


model-serving-templates/
  config.example.yaml
  docs/
    overview.md
    patterns/
      pattern-01-*.md
    checklists/
      pre-deployment.md
  templates/
    config.yaml

Support

For questions or issues, contact: megafolder122122@hotmail.com

License

MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.

📄 Code Sample .yaml preview

config.example.yaml # Model Serving Templates - Example Configuration # Copy this file to config.yaml and update values for your environment serving: framework: "fastapi" # fastapi or flask host: "0.0.0.0" port: 8000 workers: 2 model: path: "./models/model.pkl" format: "sklearn" # sklearn, pytorch, tensorflow, onnx warm_up: true warm_up_requests: 5 inference: batch: enabled: false max_batch_size: 32 max_wait_ms: 50 timeout_seconds: 30 ab_testing: enabled: false variants: - name: "control" model_path: "./models/model_v1.pkl" weight: 80 - name: "treatment" model_path: "./models/model_v2.pkl" weight: 20 logging: level: "INFO" request_logging: true
Buy Now — $49 Back to Products