Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
Why feature engineering still matters in the deep learning era
Deep learning automates feature extraction - but it does not remove the need to think carefully about what data you feed into the model.
ML models decay silently - and most companies do not notice
A model that was accurate at launch will gradually stop being accurate as the world changes. Why monitoring for model decay is not optional, and how to set it up before it becomes an incident.
BERT and the new baseline for applied NLP
What the BERT model changes in the practical use of text processing, and why it matters for companies working with unstructured data.
On-premise vs cloud: most companies end up with both
The debate between keeping servers in-house and moving everything to the cloud rarely ends with a clean answer. A look at what a realistic hybrid posture actually involves.
Who owns data quality in a company that is not a data company
Data quality problems are common. Accountability for them is rare. A look at how to assign ownership without creating a bureaucratic layer that nobody uses.
The real cost of adopting Kubernetes
What companies fail to account for when deciding to move to Kubernetes: not just technical complexity, but organisational and staffing challenges too.
Streaming data: when you need it and when batch is enough
How to decide whether your company needs streaming data processing, or whether that is unnecessary complexity for tasks that batch loading handles perfectly well.
When an internal API gateway actually helps
API gateways are standard at the public perimeter. The question of whether to put one between your own internal services is less obvious and worth thinking through.
Narrow AI in production: where the line between pilot and working system is
Why most AI pilots never reach production, and what it actually takes for a model to work in real conditions rather than just in a demo.
GDPR: lessons from the first months of enforcement
What the first weeks of real GDPR enforcement revealed, and how it changes the practical approach to handling personal data.
When to split a monolith into microservices - and when not to
A practical look at the moment when architectural decomposition is justified, and when it creates more problems than it solves.
Data warehouse or data lake: how to make the right call
A breakdown of two architectural approaches to corporate data storage and the criteria that actually matter for mid-size companies.