How To Organize Data Science Teams

One question I often come across is how to best organize cross functional data science teams where engineers and data scientists are working closely together. Here is what I’ve seen to work well in practice.

Machine Learning As The Ultimate Test Driven Development

As machine learning is becoming more mainstream (well that’s already long past I guess) more and more teams who are new to ML are attempting to run data science projects. One of the most common mistakes is to think that ML is “just another library” so that people are approaching a data science project like … Continue reading Machine Learning As The Ultimate Test Driven Development →

How the data science toolset is getting in the way of doing 5-10 experiments per week

I’m re-reading Inspired by Marty Cagan lately and came across this quote: “To set your expectations, strong teams normally test many product ideas each week-on the order of 10 to 20 or more per week.” To be honest I was pretty shocked.

Why Recipes for Machine Learning Solutions Don’t Work

People who ask me “how do you solve a certain project with machine learning” often expect some kind of a recipe as if they were baking a cake. I understand the expectation and often find myself trying to give something as close to a recipe, but lately I have come to realize that the answer is more a process than a recipe.

When Big Data became Scalable Databases

Cleaning up my bookshelf a bit, I came upon the book Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. It deals with all kinds of ways to deal with big data sets, data streams, link structures between documents, social network analysis, and other kinds of data which of occur in large amounts. There … Continue reading When Big Data became Scalable Databases →

Wardley Maps and the Democratization of AI

There is one book I've read in the past months that keeps coming back to me. Currently in the form of a series of Medium posts by Simon Wardley it is already a few hundred pages in length. Created as a means to map out the different parts of a company and to help developing … Continue reading Wardley Maps and the Democratization of AI →

Key AI Trends for 2020

I recently did a podcast with Ben Lorica for our new project The Data Exchange on key AI trends for 2020. I particularly happy to be part of this project so we continue to collaborate now that he has left O'Reilly and joined databricks. So I checked, and actually I met him for the first … Continue reading Key AI Trends for 2020 →

My 2019 Recap of Machine Learning From Academia To Industry

In a few days on August 1st, I will have completed my fourth year at Zalando. It is my first job out of university and I was fortunate enough to have their trust to make the switch from supervising a bunch of Ph.D. students to managing teams and leading people. Later, I switched to an … Continue reading My 2019 Recap of Machine Learning From Academia To Industry →

The Levels of Doing AI

When it comes to new technologies like Artificial Intelligence, the pure technology is only a small aspect required to putting it to use. Still, given the hype that exists currently, one can easily loose sight of the big picture as announcements of new algorithms, toolboxes, or cloud services fight for grabbing our attention as the … Continue reading The Levels of Doing AI →

But is it AI?

One discussion I find myself in more often recently is people asking me whether something is "really AI" or not. Often, what people seem to mean with that is whether someone is already using deep learning, or still "just" machine learning. I mentioned this to a friend in the industry and he just rolled his … Continue reading But is it AI? →