MLOps
Delivery, observability, CI/CD, governance and the operational backbone of ML systems.
Topics 7 posts 2 posts 1 posts 10 posts 1 posts 4 posts 2 posts 2 posts
A map of the problems worth writing about
The site stays opinionated: fewer topics, more depth, and a consistent bias toward systems that need to be maintainable under production pressure.
Data Engineering
Pipelines, lakehouse architecture, orchestration and the less glamorous layers that keep data products alive.
Data Architecture
Data contracts, modelled data products, quality boundaries and the design decisions that make a platform trustworthy.
AI Architecture
Reference architectures, trade-offs and platform decisions behind production AI systems.
Azure
Applied Azure patterns for ML, data platforms, security and delivery workflows.
Databricks
Operational Databricks patterns for apps, AI Search, data products and production-facing ML or GenAI systems.
Observability
Monitoring, feedback loops, data quality signals and the runtime visibility needed to keep ML systems trustworthy.
Tutorials
Step-by-step implementation notes focused on shipping, not slideware.