Model Registry – Managing and Versioning ML Models
30 Days of MLOps Challenge: Day 20
By Sandip Das · · 5 min read
💡
Hey — It's Sandip Das 👋
We should learn Model Registry to systematically manage, version, and track ML models across their lifecycle, ensuring reproducibility and smooth transitions between development, staging, and production.
It enables better collaboration, auditability, and automated model promotion in real-world ML workflows.
We should learn Model Registry to systematically manage, version, and track ML models across their lifecycle, ensuring reproducibility and smooth transitions between development, staging, and production.
It enables better collaboration, auditability, and automated model promotion in real-world ML workflows.
📚 Key Learnings
- Understand the role of a Model Registry in production ML systems
- Learn how to track, store, and version models throughout the ML lifecycle
- Discover how to promote models between stages (e.g., Staging → Production)
- Use tools like
Read the full article on learnxops.com.
Topics: MLOps, ML Model Registry, Community, 30 Days of MLOps