MLOps is a really hot topic right now and there are many products out there that promise to meet all your needs to put ML algorithms in production. At the same time, many if not most ML projects still struggle to make it to production and teams have a hard time managing a large number … Continue reading In Search for the Holy Grail of MLOps: Taming the Beast of ML Use Cases
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.
FOR REASONS I’m reading a lot of startup literature right now, including Zero to Sold, The Mom Test, Four Steps to the Epiphany, and The Lean Startup. One thing all of these books have in common is that they say the old line of “build and they will come“ is wrong. Considering myself a technical … Continue reading A Plea for the Technical Founder
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
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 … Continue reading What is Data Science/Product Fit And Why You Need It
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.
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.
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.
I have a hunch that once people saw the economic potential of software, they started looking for ways to "scale it up" and we haven't stopped searching yet.
Based on this twitter thread. One thing that always makes me think are the roles we have in the software industry. The bigger the company, the more specialized the roles it seems. In data science, I‘ve seen charts with data analysts, data scientists, ML engineers, MLOps, ... some thoughts. How do you come up with … Continue reading Don’t Do Roles (Only) By Skillset
Marginally Interesting by Mikio L. Braun 