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ML Engineer @ Tooploox, Data Science Instructor @ DataCamp, michaloleszak.github.io, Book 1:1 @ hiretheauthor.com/michal

Welcome to my Medium page!

Bio

Hello there! I’m Michał, a Machine Learning Engineer. I do magic with data and statistics. On Medium, I write about all things data: machine learning algos, statistical rigor, best practices, MLOps, how-(not)-to-do-stuff, and more. You can find out more about me on my website.

Consulting

I’m available for consulting. You can…


Regression discontinuity designs

Image by the author.

The word “because” tends to get overused significantly. We often don’t realize the strength of its meaning. “Because” implies causality — the relationship between cause and effect, which takes some statistical virtuosity to establish.

This is the third article in a series in which I discuss four statistical tools that…


Enforcing randomness via instrumental variables

Image by the author.

Whenever someone says the word “because”, they imply a causal impact. Something is causing some other thing to occur. It wouldn’t have happened otherwise. Such because-statements are pretty bold ones, as it takes some statistical virtuosity to correctly establish causal effects. …


The golden standard of randomized experiments

Image by the author.

Being able to establish causality is powerful. It gives you the right to use the word “because” in a conversation. Our sales increased because we have changed the website layout. The crime rate dropped because of the new preventive policy that has been introduced. Pinpointing causal relations correctly is crucial…


Tips and Tricks

Should you “swish” to these new inventions, or stay with the oldies but goldies?

The pace of scientific progress in the field of machine learning is unparalleled these days. It is quite hard to stay up-to-date, unless only within a narrow niche. Every now and then, a new paper pops up claiming to have achieved some state-of-the-art results. …


Artificial Intelligence

Five practical lessons and warnings for data scientists

Photo by Viktor Forgacs on Unsplash

The sudden hit of the COVID-19 pandemic found the doctors and the hospitals completely unprepared. Too little was known about the new virus and too many patients were queueing at the door to diagnose and triage them correctly and quickly enough. AI to the rescue!

Machine learning models for diagnosing…


MLOps

What to watch out for while maintaining ML systems

Some of the problems described in “Hidden Technical Debt in Machine Learning Systems”, image by the author.

It is not easy to develop and deploy machine learning models, and even less so to integrate them with the surrounding data pipelines to build large-scale ML systems. The hardest part, however, comes later, when the entire system has been tested, deployed, and is up and running. For deployment is…


MLOps

Tackling data drift and concept drift in production ML systems

Photo by Danny Sleeuwenhoek on Unsplash

You have collected and cleaned your data, experimented with various machine learning models and data preprocessing variants and fine-tuned your model’s hyperparameters to finally come up with a solution good enough for your problem. Then, you’ve built a robust, automatic data pipeline, wrote an API for the model, put it…


A statistician’s take on which countries will advance to the knockout phase and what (not) to bet one’s money on

Photo by Janosch Diggelmann on Unsplash

With the Champions League final having concluded club games across Europe, all football fans’ attention shifts to the national teams and the European Championships that is to start on June 11. Originally scheduled for 2020 but postponed for a year due to the pandemic, the ‘Euro 2020’ tournament will crown…


Follow these steps to make your Python classes bulletproof

Photo by Julian Myles on Unsplash. The objects have not been designed well and have collapsed.

Object-oriented programming, or OOP, is a popular coding paradigm as it ensures code reusability and prevents redundancy. Its appeal made it popular outside of software engineering, in the fields such as data science and machine learning engineering. There is, however, a huge difference between designing an object that just works…

Michał Oleszak

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