#ml engineers

Many AI initiatives fail after the prototype phase—not because the model is weak, but because the deployment pipeline is missing. This guide breaks down the real engineering work behind successful AI productization: data orchestration, CI/CD for ML, testing frameworks, latency management, drift detection, and post‑deployment optimization. You’ll learn what separates models that succeed in production from those that remain stuck in development.