ML in Practice - Issue #9
It has been a long while since the last newsletter. I've continued to work on projects with several clients, mentoring data science teams as well as early stage startup CTOs. My biggest project went on a break early March and will likely continue in a couple of weeks.
I actually took the time to code again, something I've always wanted ever since I started to be a consultant. I tried many times, and it turned out pretty hard to silence that little voice in your head that says "isn't this all bullsh*t?" and "are you really looking up how to do this basic thing in Python on stackoverflow right now?!"
Luckily this time I could drown out that voice and dive in and I have to say I completely forgot how refreshing coding can be.
5 Posts on Data Science You Might've Missed
I've also spent some time into writing again. I find it interesting that increasingly my thoughts are around the best way to run data science projects, and less about the latest ML framework. It's not that I don't see room for improvements on the tooling side, but in my experience working with all kinds of teams, getting the teams set up and approaching a project in the right way is the most important thing to get right.
Why Recipes for Machine Learning Solutions Don’t Work — mikiobraun.wordpress.com 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…
How the data science toolset is getting in the way of doing 5-10 experiments per week — mikiobraun.wordpress.com 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.
How To Hire A Data Scientist — mikiobraun.wordpress.com Running data science projects successfully is already quite challenging, and the first step is to hire some data scientists. Here are my answers to questions I often get asked.
What is Data Science/Product Fit And Why You Need It — mikiobraun.wordpress.com I always felt that Data Science and Product Management share a lot of research aspects. When developing a new product, you have to work with assumptions, uncertainties, and gradually figure out a path to validate or verify these assumptions. You need to adapt till you have found something that solves a relevant problem for the…
Machine Learning As The Ultimate Test Driven Development — mikiobraun.wordpress.com
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…
What's up next?
I honestly cannot tell, but I the last 1.5 years of consulting have been very insightful. I think the question is what kind of work I'd like to do and where I see the biggest opportunity.
Till these things become clearer to me, thanks for sticking to this newsletter!