Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
Monolith or services: how to make the call without an engineering background
A practical explanation for founders and executives: when splitting your architecture into services is justified, and when it just adds cost.
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.
Event-driven architecture and data contracts: why data is no longer a by-product
Moving from synchronous integrations to event-driven architecture changes how data is treated. Data contracts become a first-class engineering artefact.
IT budget for 2020: splitting between maintenance and growth
Year-end is budget planning season. How to think about allocating IT spend between what already exists and what needs to be built.
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.
Contractor and vendor access: an underestimated risk point
Third parties with access to your systems are one of the least-controlled security perimeters. How to think about this from a management perspective.
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.
Data as a product: why you cannot put one team in charge of all the data
When analytics stops working, the problem is usually not the tools. How to distribute data responsibility across teams.
When you should not break up the monolith
Microservices sound modern, but decomposing a monolith without sufficient reasons creates more problems than it solves. How to think about this decision.
Autonomous mobile robots in the warehouse: running the economics
AMRs are no longer a future concept - they are a working tool. I look at when they pay off and when buying a robot turns out to be an expensive mistake.
The Capital One breach: the cloud is not to blame, configuration is
In July 2019 Capital One lost data on over 100 million customers. I look at what happened and why the main lesson is not about the cloud - it is about access management.
Why ML teams keep rewriting the same thing over and over
Feature stores and feature management in machine learning: where the duplication comes from and how to get rid of it.