Machine Learning in Production? What This Really Means

Putting a machine learning model into production means its outputs directly impact a user or product, with systems in place for accountability and correction. This process involves more than just the model itself; it is part of a larger data pipeline and requires a shift in mindset from research to engineering. The key steps include wrapping the model in a function, exposing it via an API, packaging the environment for portability with tools like Docker, and deploying it on stable infrastructure. Finally, continuous monitoring is essential to track service health, data drift, and business impact, as performance can quietly degrade over time. The most effective production model is often the one that best fits real-world constraints like latency, cost, and maintainability, rather than the one with the highest offline accuracy.