Microsoft Azure
Cloud platform
Selected work
Code and workshops behind the writing
Public examples you can inspect and run, with the design decisions and limits made explicit.
Runnable tutorial
GTD MCP Server
An MCP server that gives an agent structured tools for tasks, projects and inbox capture.
The idea behind it
Keep task logic separate from transport and storage, with local SQLite and Databricks deployment paths.
What you can run
Follow the quickstart to run locally, then explore the Databricks Apps setup and its service-principal permissions.
Scope and limits
An educational example. A shared deployment needs its own review of user identity, data isolation, access rules and recovery.
Workshop materials
AI/ML in Practice
Nine learning modules connect data preparation and feature engineering to training, pipelines, MLOps and generative AI.
The idea behind it
Follow the dependencies around a model, from its input data to a repeatable delivery workflow.
What you can run
Use the repository setup instructions to import the materials into Databricks Free Edition. Start with data preparation, then work through training and pipelines.
Scope and limits
Exercises for learning and workshops. They do not constitute a complete production platform or an operational service commitment.
Working stack
Core technologies behind the work
The stack is deliberately practical: cloud platform, lakehouse, orchestration, automation and writing tools that support repeatable delivery.
Microsoft Fabric
Data engineering and analytics
Microsoft Foundry
AI applications and agents
Databricks
Lakehouse and orchestration
Python
Automation and platform code
PySpark
Data processing
SQL Server
Data modeling and query work
MLflow
Experiment and model lifecycle
GitHub Actions
CI/CD workflows
Terraform
Infrastructure automation
Docker
Containers and portable runtime environments
GitHub
All projects live on GitHub
Repositories, experiments, demos and supporting code are all available on GitHub.