The gap between a successful proof-of-concept and a reliable production ML system is vast. MLOps aims to bridge this gap.
Model Drift and Monitoring
Unlike traditional software, ML models degrade over time as the real-world data changes. Continuous monitoring for data drift and concept drift is essential.
Automated Retraining Pipelines
Build robust pipelines that can automatically ingest new data, retrain the model, and deploy it if it meets performance benchmarks.