Machine Learning Canvas

Why the Machine Learning Canvas exists

In large organizations, machine learning efforts often struggle not because of technical limits, but because teams lack a shared way to frame use cases, assumptions, and value.

The Machine Learning Canvas was created to give teams a common structure to reason about ML work — before models are built, and before resources are committed.

Used by practitioners across large organizations

“To dissect use cases, we recommend the Machine Learning Canvas”
— Carlos Escapa, Global AI/ML Practice Lead at Amazon Web Services
“Best framework I’ve seen to help clients discover uses for their data”
— Diego Ventura, Customer Success at MonkeyLearn, Inc
“The Canvas allowed us to structure and focus our delivery sprints.”
— Christian A. Schiller, Senior IT & Enterprise Architect at Deutsche Telekom

The Machine Learning Canvas has been used in industry, consulting, and academic contexts since 2015. If you need to reference it formally (e.g. in documentation or publications), you may use the following citation:

@misc{mlc,
    title  = "Machine Learning Canvas",
    author = "Louis Dorard (OWNML)",
    url    = "https://www.ownml.co/"
    year   = "2015",
}

The Machine Learning Canvas is part of a broader approach to bring discipline to enterprise machine learning.