Longer essays and technical guides on MLOps, data engineering, AI architecture, Azure and the platform concerns that tend to show up after the model finally works.
Medallion architecture for engineers: design raw data, trusted views and published data products with clear contracts, replay paths and ownership.
AI-generated code risks are often not obvious syntax errors. The harder problem is code that catches real failures, retries them badly, returns mock data and still reports success.
A practical guide to production ML delivery on Databricks: where GitHub Actions fits, why Declarative Automation Bundles should usually be the deployment contract, and what to standardize before the first real production rollout.
A practical guide to MLflow-centered ML delivery patterns: when Azure Machine Learning is the better operating model, when Databricks fits better, and what actually survives production teams and real environments.
A practical guide for architects and ML teams: what a feature store is, when you need one, which feature store use cases are compelling, and which simpler alternatives are often enough.
A practical Databricks MCP server tutorial: build a custom MCP server in Python, keep the tool layer small, and deploy it on Databricks Apps with a Delta-backed GTD assistant.
A practical starting point for ML observability: what to monitor first, which signals matter early, and how to avoid building a monitoring stack that is bigger than the model itself.
If you ask whether MLOps is mostly software engineering, the practical answer is broader: delivery, data reliability, architecture and operational ownership usually matter more than the model code itself.
A practical definition of MLOps and a clearer view of where it starts, what it covers and why teams keep reducing it to tooling or deployment.
A practical guide to the systems, delivery and MLOps concerns that start after a model works in a notebook.
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