Blog
How to build production-grade retrieval-augmented generation (RAG) on Azure and Databricks: data preparation, retrieval, security, evaluation, and operations.
A practical framework for enterprise data science: start with the decision, build trustworthy features, validate rigorously, and embed predictions where people work.
A practical enterprise blueprint for bronze, silver, and gold layers on Azure Databricks with Delta Lake, Lakeflow, and Unity Catalog governance.
An enterprise MLOps playbook on Azure Databricks: MLflow 3 tracking, Unity Catalog model governance, Model Serving, CI/CD, and production monitoring.
Seven proven data engineering practices for reliable Azure and Databricks pipelines: incremental loads, data contracts, quality checks, orchestration, CI/CD, observability, and cost control.