Here I'm collecting some of my thoughts around how to get better at building machine learning based products and services.
I think this is still a somewhat new area and we're collectively still figuring out how to do this. By collecting my thoughts here I am hoping to help others who are facing similar challenges.
I've personally worked on many of the aspects I think are relevant for doing machine learning "in practice".
- Originally, I started computer science and some of my favorite topics are programming languages and distributed system design.
- I hold a Ph.D. in machine learning and worked in applied and basic machine learning research for more than 10 years.
- I led teams building machine learning projects for several years.
- I have worked in a individual contributor role (a.k.a. principal or staff engineer) with machine learning teams. I did this from a department level up to some topics on a company level.
- I've recently started to consult companies around the topic.
For some reason, I'm seldom content with learning one thing well, but once that happens I'm always asking myself "but what now." This has taken me from fundamental research to the industry. I also have a hard time keeping myself to look beyond whatever scope I am in to figure out what the real solution to a problem is. I think both of these habits have driven me to look into the big picture when it comes to ML in practice and I hope that helps you, too.
I currently expect that this will rather become a collection of essays than a self-contained book.