Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
AI in 2019: what actually moved and what stayed a promise
A year-end assessment for people making adoption decisions. Without hype - what became a production norm, what is still on the way.
NLP in production: the gap between a demo and a working system
Language models in 2019 deliver impressive demonstrations. Why the road from demo to a real working product is much longer than it looks.
Russia's national AI strategy: what it means for industry adoption
In October 2019 Russia approved a National AI Development Strategy through 2030. I look at what is practically meaningful for companies thinking about adoption right now.
GPT-2 and language models: what the signal means for business right now
After GPT-2, the conversation about text generation shifted. I look at what actually changes for companies today and what is still in the lab.
AutoML: what it is and what a manager should not expect from it
How AutoML tools lower the barrier to machine learning - and where they still require expertise and management decisions.
The real cost of an NLP pipeline before you are sold by the demo
What actually requires ongoing support in a production NLP system - from data labelling to quality control in live operation.
Model drift: why an ML system degrades without visible failures
Machine learning models in production lose accuracy over time - quietly, with no errors and no alerts. What drift is and how to monitor for it.
From hype to inference cost: why AI must be measured as a production function
How to move from evaluating AI by its demo effect to evaluating it by the real economics of running a model in production.
Why AI projects die before they produce results: five recurring patterns
An analysis of the typical reasons AI initiatives stall or fail to deliver their promised impact - and what to do about it.
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.
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.