Still holds true! And yes: Humblebee is always hiring people who are dabbling with code and new technology (like ML). Source:

Machine Learning for the impatient

Toy around, learn, build your first machine learning project, and get resources for your next steps

Mikael Vesavuori
10 min readMar 10, 2020


There are quite a few uphill battles if you want to delve into machine learning: understanding basic statistics, probably needing to learn Python, getting to know all of the data science libraries like Pandas, having a grasp of what ML is and what it can do, and so on and so forth. While it’s all quite doable it can be pretty discouraging to jump so many hoops just to get something (at all!) done.

In this article I will present a number of AI/ML-related in-house micro projects we’ve completed at Humblebee as part of dipping our toes into ML. If you are a developer I think you should probably be able to complete these kinds of projects in an evening or so.

The tools I present are powerful, easy-to-use, rather conventional machine learning-based APIs. Custom functionality is still something you will (for the next few years) rely on building on your own, though advances like Google Cloud AutoML, Amazon Sagemaker Autopilot, and Azure Machine Learning Designer are paving the road to a crowd with less data scientist-heavy ranks.

Caveat that I am in no way an “expert” in the area with 10 years of experience and a dual-PhD in data science. However, as Google have been promoting aggressively, the area of ML cannot be and should not be entirely given up to only one specific subsector (data scientists) since it has a big overlap onto so many other areas as well — not the least, the spillover onto development.

Things you won’t need:

  1. Math wizardry skills
  2. A PhD degree
  3. An expensive, nitro-fueled computer
  4. Tedious local environments
  5. Bank full of money

Much has been written on the distinctions between regular programming, machine learning, and artificial intelligence. A helpful metaphor that I like is that programming is about solving problems based in logic, rules and flow. Machine learning is a way of using statistical models to understand (predict, often) new data from knowledge of previous data. Artificial intelligence is the wider field of computers autonomously



Mikael Vesavuori

Technical Standards Lead at Polestar