MLOps Best Practices for Production
Getting a model to production is just the beginning. MLOps provides the framework and practices needed to reliably deploy, monitor, and maintain ML systems at scale, bridging the gap between data science experiments and production engineering.
Core Practices
Version Everything
Track code, data, models, and configs with proper versioning
Automate Pipelines
CI/CD for training, testing, and deployment workflows
Monitor Continuously
Track model performance, data drift, and system health
Ensure Governance
Implement access controls, audit trails, and compliance
Pro Tip
Start with a minimal MLOps setup and evolve as needed. Over-engineering your infrastructure early can slow down iteration and increase complexity without proportional benefits.
Lisa Thompson
MLOps Engineer
Lisa has built ML platforms at Fortune 500 companies and now leads MLOps education initiatives at 1.ML.