How to build your AI startup

Timid New World

As the coronavirus pandemic escalated, I was travelling around the world. The timing was unfortunate, but the endless hours of flying also presented an opportunity for reflecting on

ML at Veriff: What is possible in a year

devclub.lv meetup 07.11.2019ML at Veriff: What is possible in a year?Google Docs

Future of e-Estonia - a young engineer’s view

Data loops are the bottleneck in applied AI

You can quickly iterate code, but not data. This is one of the major bottlenecks in companies trying to automate human activities, and solving it generally would change

Building automation-heavy products

This post is based on my talk at North Star AI 2019 [https://aiconf.tech/]. When I speak about automation in general, and the Automation team at Veriff,

The two loops of building algorithmic products

This is a talk I gave at a Zen of Data Teams [https://www.meetup.com/Machine-Learning-Estonia/events/260081030/] event in Tallinn. The full slides are here [https:

Why I Gave Up a 50% Higher Salary to Join a Startup

Now the title might be a bit clickbaity but it’s true: when I joined Veriff I had received an offer with a higher salary from a larger

Units of automation

Pretty much every presentation I give about AI includes the following slide, in the context of automatically authenticating users using face recognition. The idea is that even automation-reliant

Writing a data science job description

It’s common to see job descriptions along the lines of the following. -------------------------------------------------------------------------------- Looking for a data scientist. Responsibilities: 1. Building big data databases, data warehouses, data