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🚀 Deployment Lab: Production Serving 🏭

The Deployment Lab is FlowyML's integrated model serving engine. It bridges the gap between a trained artifact and a live production endpoint, allowing you to deploy REST APIs with a single click.

🛠️ The Deployment Lifecycle

graph LR
    A[Model Artifact] --> B{Deployment Lab}
    B --> C[REST Endpoint]
    C --> D[Live Predictions]

    style B fill:#f9f,stroke:#333,stroke-width:4px

1. Artifact Selection

From the Assets tab, choose any Model artifact. FlowyML automatically identifies the framework (Keras, PyTorch, Scikit-Learn) and prepares the runtime environment.

2. Configuration

The Lab allows you to tune your endpoint for production requirements: - API Token: Secure your endpoint with auto-generated Bearer tokens. - Rate Limiting: Prevent abuse by capping requests per minute (RPM). - Time-to-Live (TTL): Perfect for ephemeral testing. Set the deployment to auto-destruct after 30 minutes to save resources.

3. Monitoring & Logs

Once live, you can monitor individual request/response cycles in the Live Logs viewer.

💻 Making Your First Prediction

FlowyML endpoints are standard REST APIs. You can consume them from any language.

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import requests

# The endpoint and token are provided in the Deployment Lab UI
URL = "http://localhost:9000/predict"
HEADERS = {"Authorization": "Bearer flowy_sk_... "}

payload = {
    "data": {
        "user_id": 123,
        "features": [1.0, 2.5, 0.3]
    }
}

response = requests.post(URL, headers=HEADERS, json=payload)
print(f"Prediction: {response.json()['result']}")

🛡️ Production Hardening

State Management

The local Deployment Lab uses in-memory processes for the MVP. For high-availability production workloads, we recommend using the Kubernetes Plugin or Vertex AI Stack, which handles container orchestration and autoscaling.

Shadow Deployments

Use the Context Switching feature in the UI to deploy a new version of a model alongside the "Standard" version. You can then route a small percentage of secondary traffic for A/B testing or "Shadow" validation.

💡 Pro Tips

Automatic Dependencies

FlowyML detects your model's requirements. If you deploy a TensorFlow model, the Lab will offer to install tensorflow in the serving process automatically.

Client SDK

Use the flowyml-client library for native Python integration with built-in retry logic and type validation.