Building Scalable ML Pipelines with MLOps - From Prototype to Production with Azure and GitHub
· 13 min read
The journey from a promising ML model in a Jupyter notebook to a production system serving millions of predictions daily is fraught with challenges. Data drift, model degradation, infrastructure scaling, and deployment complexity are just a few hurdles that can derail even the most promising AI initiatives.
In this comprehensive guide, we'll build a complete MLOps pipeline using Azure DevOps and GitHub Actions, demonstrating how to automate model training, validation, deployment, and monitoring at enterprise scale. By the end, you'll have a blueprint for transforming your ML experiments into robust, production-ready systems.