From Notebook to Production: MLOps with MLflow and Azure

Training a good model is the easy part. Keeping it accurate, reproducible, and running in production is where most ML projects stall. A lightweight MLOps setup on Databricks and Azure closes that gap.

Track every experiment

MLflow records parameters, metrics, code versions, and artifacts for every run, so you can always answer "which model is this and how was it built?"

Register and govern models

Models registered in Unity Catalog get versioning, aliases such as "champion" and "challenger", and the same access controls as your data.

Serve with confidence

Databricks Model Serving or Azure Machine Learning managed endpoints expose models as scalable REST APIs, with autoscaling and no servers to manage.

Monitor what matters

Track input drift, prediction quality, and latency after deployment. When performance drops, the same pipeline that trained the model can retrain and promote a new version through automated CI/CD.

Add generative AI where it fits

Microsoft Foundry (formerly Azure AI Foundry) and Azure OpenAI make it practical to build retrieval-augmented generation (RAG) apps grounded in your own governed lakehouse data, rather than a model's general knowledge.

The goal isn't more models. It's models the business can depend on.