MLOps Best Practices: Keeping Your AI Healthy

Building a model is fun. Deploying it? That's where the real work begins. I've learned the hard way that a model in a notebook is very different from a model in production.

It's Not Just Code, It's Data

In traditional software, if the code doesn't change, the output doesn't change. In ML, your code can be perfect, but if the data drifts, your model breaks. That's why MLOps is so critical.

The Three Pillars I Swear By

• Reproducibility: Can I train this exact model again in 6 months? If the answer is "maybe," you have a problem. Version your data, not just your code.
• Automation: Manual deployments are a recipe for disaster. CI/CD pipelines for ML are a must-have, not a nice-to-have.
• Monitoring: You need to know when your model starts acting weird before your users do. Track drift, latency, and error rates relentlessly.

MLOps is about bringing the discipline of engineering to the art of data science. It's what separates a cool experiment from a reliable product.