MLOps
35 min read
MLOps Fundamentals
DevOps for ML
Building an AI model is one thing. Deploying it reliably at scale is another challenge entirely. MLOps—machine learning operations—is the discipline of getting AI from notebook to production.
The Production Gap
90% of AI projects never make it to production. Why? Training a model in a notebook is the easy part. Production requires: handling variable traffic, monitoring for drift, rolling back bad models, serving efficiently, managing versions, and maintaining security. MLOps provides the frameworks and practices to solve these challenges.
Core MLOps Practices
Version control for data and models (not just code). Automated training pipelines that can reproduce any experiment. Continuous integration for ML—test not just code, but model quality. Deployment patterns like blue-green and canary. Monitoring for data drift, performance degradation, and business metrics.
Tools of the Trade
MLflow for experiment tracking. DVC for data versioning. Kubernetes for orchestration. Feature stores like Feast for feature management. Monitoring tools like Evidently for drift detection. The ecosystem is large, but the principles are consistent.
💡 Key Takeaways
- Most AI projects fail at the production step
- MLOps = DevOps practices adapted for ML
- Version everything: code, data, models, configs
- Monitor continuously: performance, drift, business metrics
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