Recent Posts

Getting my MLOps Thoughts Straight

MLOps has a lot of moving parts, I’m trying to make sure I’ve given them all a spot in my thoughts. I’ve written one blogpost about MLOps previously, which was like an introduction to the subject I guess.

Responsible ML Workflow summary

I recently came across this paper on LinkedIn, which I thought was a particularly valuable source, both in itself and for further reading. I enjoyed reading it and learned a lot of new things, made a summary for myself and figured I would share it.

MLOps: Not as Boring as it Souds

Have you heard of MLOps? When I first heard the term, I admit my first reaction was; ‘Boring!’, and I rolled my eyes like you’re supposed to when you say stuff like that.


Work with data and code? Do you ever need to report your results?

By linking your reports to your runs, results and code, you never have to manually update a number or plot again.

Using MLFlow and Jupyter Book (or R Bookdown) and a few simple tricks to organise your workflow, it’s easy to keep track of your code, your reports and your runs so that you can always reproduce and retrace any number or plot you’ll ever create or report.

BookFlow example BookFlow utiliy package

BookFlow Guide (Jupyter) Coming soon: BookFlow Guide for R Bookdown

dr. Jeroen Franse

Data Scientist


Astrophysicist turned data scientist. Enjoys thinking about all the things that could be wrong with his models. Has strong opinions on how to organise data science work after seeing too many disillusioned academics and corporate data scientists. Believes machine learning and big data will cause more harm than good if not regulated. Sleeps with one eye open. Usually hides his cynicism better than this.


  • Responsible Data Science
  • MLOps
  • Astrophysics


  • PhD in Astrophysics, 2016

    Leiden University

  • MSc in Astrophysics, cum laude, 2012

    Leiden University

  • BSc in Astrophysics, 2010

    Leiden University