Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
AI readiness: what companies confuse with actual preparation
Why the gap between interest in AI and operational readiness to deploy it is much larger than it appears after a conference or a demo.
Why ML teams keep rebuilding the same data pipelines
The hidden cost of ML at scale is not the models - it is the duplicated feature engineering work every team does independently. What a feature store is and whether you actually need one.
ML in production: the gap between a pilot and a working system
Why machine learning pilots often fail to become production systems, and what to do differently from the very beginning.
The Transformer architecture: a new universal foundation for sequence processing
What the arrival of the Transformer architecture means for companies thinking about applying language models in their processes.
The gap between an ML experiment and a production system
Why machine learning in a notebook and machine learning in a running product are different tasks with different requirements.
Feature engineering is a business decision in disguise
The variables you feed into a machine learning model are not a purely technical choice. They encode assumptions about your business that deserve explicit review.
Chatbots: between the hype and the first practical use
In 2016 everyone is talking about chatbots. Here is where they actually work and where they are a marketing promise.
TensorFlow and open source: what actually changed for companies
Why Google opening its ML framework shifts the conversation from 'we can't afford it' to 'we need data and an engineer'.
AlphaGo and the shift in what we expect from AI
AlphaGo's win over Lee Sedol is not just a technical result. It is the moment when the AI conversation stops being only about recognition.
TensorFlow goes open source: what changes for non-researchers
Google opened TensorFlow in November 2015. I look at what this means for companies that are not in the business of academic research.
TensorFlow and the shift of machine learning from research to engineering
What Google's open release of TensorFlow changes for companies: pipeline, reproducibility, and deployment become the central question, not algorithms.
NLP text classification as a practical enterprise baseline
Before the deep learning wave reshaped NLP, classical text classification already solved real problems. What it does well, where it stops, and how to start.