Not a dumping ground for every experiment. This section is for work that supports talks, articles and production-minded platform design.
Architecture work for Azure-native data platforms, lakehouse setups, MLOps foundations and AI delivery systems that need clear ownership and operational boundaries.
Small but credible repos built to support talks, workshops and technical writing without collapsing into toy examples that ignore deployment reality.
Delivery helpers, orchestration patterns, CI/CD templates, observability layers and practical automation around cloud-native ML and data systems.
The stack is deliberately practical: cloud platform, lakehouse, orchestration, automation and writing tools that support repeatable delivery.
Cloud platform
Lakehouse and orchestration
Automation and platform code
Data processing
Data modeling and query work
Experiment and model lifecycle
CI/CD workflows
Infrastructure automation
Containers and portable runtime environments
Repositories, experiments, demos and supporting code are all available on GitHub.