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By heissanjay · Published 2/1/2026

From Notebook to Production ML

A practical workflow for turning experiments into maintainable ML services.

1 min read

• ML Engineering
• MLOps
• Systems

This draft outlines a production workflow:

1. Define success metrics before model work starts.
2. Build a repeatable evaluation harness.
3. Ship with observability and rollback plans.

Baseline Service Contract

Start with a clear service boundary before model experimentation:

from pydantic import BaseModel

class InferenceRequest(BaseModel):
    prompt: str
    user_id: str

class InferenceResponse(BaseModel):
    answer: str
    latency_ms: int

Deployment Health Checklist

A fast model iteration loop is only useful when deployment feedback is reliable.