ML in Production: Lessons from Deploying 20+ Models at Scale

Machine learning in production is fundamentally different from ML in research. After deploying over 20 models into production environments, here are the patterns that matter most.

Infrastructure Is Half the Battle

A model that works in a Jupyter notebook will break in production. Invest in proper infrastructure: feature stores for consistent transformations, model registries for versioning, and containerized deployment pipelines that make rollbacks trivial.

Monitoring Isn't Optional

Models degrade in production. Data drift, concept drift, and infrastructure failures are inevitable. Build monitoring that tracks not just system metrics but model performance metrics — accuracy, latency, and prediction distributions — and alerts you before your users notice problems.

Governance Scales Trust

As you deploy more models, governance becomes critical. Every model needs documentation: what data it was trained on, what its limitations are, how it should be used, and how it's performing. This isn't bureaucracy — it's what allows you to deploy models with confidence in regulated environments.

Start Simple, Iterate Fast

Don't try to build the perfect ML platform on day one. Start with a single model in production, learn from the experience, and iterate. The best infrastructure decisions come from understanding what actually breaks in production, not from theoretical architectures.