Architectural guide
Data platform architectures: what changes when AI needs your data?
Understand warehouses, lakes, lakehouses, mesh and fabric through one business example, and learn what makes a data platform ready for AI.
Beyond the Notebook
Maciej Kępa
Data & AI Architect
I design data platforms and AI systems for production. I write about delivery, observability and the decisions that keep those systems maintainable.
Current focus
MLOps & AI-ready data platforms
Practical notes on operating AI systems under real production constraints.
Platforms
Azure Databricks
Topics
AI Data MLOps
Public work
Articles Conference talks
Featured writing 
Long-form notes for engineers who have to ship
Architecture essays, delivery guides and operational lessons from the part of AI work that starts after the first promising notebook.
Architectural guide
Data warehouse: where business data gets its meaning
See how a data warehouse gives reports and AI consistent business data, where historical correctness matters, and when a warehouse is enough.
Architectural guide
ML model deployment patterns: batch jobs, functions and managed endpoints
Compare ML model deployment patterns with MLflow: batch jobs, serverless functions, container APIs and managed endpoints for production inference.
Selected work Runnable tutorial Workshop materials
Explore the code behind the ideas
GTD MCP Server
An MCP server that gives an agent structured tools for tasks, projects and inbox capture.
Setup, decisions and limitations →AI/ML in Practice
Nine learning modules connect data preparation and feature engineering to training, pipelines, MLOps and generative AI.
Setup, decisions and limitations → Topics
Choose a production AI reading path
13 posts
Production MLOps
From model packaging to deployment, ownership and operational readiness.
Explore MLOps → 3 postsAI-ready data platforms
Business definitions, data history and ownership behind reliable AI inputs.
Explore data platforms → 3 postsML Observability
Monitoring signals, feedback loops and operational baselines.
Explore observability → Speaking
Selected talks
Conference sessions on production AI, MLOps, observability and data platform architecture.
SQL Server and the Vector Revolution - how LLMs see your data
Embeddings, vector search and the bridge between traditional data platforms and modern AI.
Trenuj, wdrażaj, nadzoruj: techniczny przewodnik po AI w Databricks
A full-day workshop following data preparation, model training, pipelines and model operations in Databricks, with hands-on exercises.
About me
Architecture, delivery and AI systems that have to hold up in production
I am a Data & AI Architect working on production data platforms, MLOps and cloud-native AI systems. My work focuses on the engineering decisions that make these systems operable, observable and maintainable.