The Machine Learning Canvas —template + handbook (free download)

A framework for innovators and visionary managers striving to design tomorrow’s Machine Learning systems

There’s an Einstein quote that I love and that perfectly applies to Machine Learning:

“The formulation of the problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill.”

I’ve seen and heard of teams who have wasted months of development time due to a poor formulation of their ML problem. Poor designs of ML systems can also cost millions of dollars when they’re run in production…

I created the Machine Learning Canvas to make it easier to ask the right questions at the beginning of an ML project, and to save people from wasting all that time and money. It’s now used and recommended by people such as Carlos Escapa, Global AI/ML Practice Lead at AWS (blog), and Bill Schmarzo, CTO of IoT & Analytics at Hitachi (blog).

The MLC is a framework that helps design ML systems properly, and serves as a key communication tool for domain experts, data scientists and engineers. I’ve been using it since 2015 when consulting for various clients, and it’s now being taught at UCL School of Management.

Today I’m releasing the first draft of my new book that’s simply called The Machine Learning Canvas. It contains everything there is to know about this framework, in a 1-hour read that summarizes years of experience and hundreds of hours of research.

I started sharing this book with my clients, and I’m now making it available to you for free. I want to make the MLC useful for all, and also to thank you for being part of my audience!

Download the book now at and spread the word!

“A very neat way think about your approach to embedding Machine Learning into your business.” 
 — Samir Sharma, CEO at datazuum
“By far the best framework I’ve seen to help clients discover uses for their data, and to keep everyone focused on the same goal.” 
 — Diego Ventura, Customer Success at MonkeyLearn, Inc

Happy reading,