The Machine Learning Canvas is a template for developing new (or documenting existing) intelligent systems based on data and machine learning. It is a visual chart with elements describing the key aspects of such systems: the value proposition, the data to learn from (to create predictive models), the utilization of predictions (to create proposed value), requirements and measures of performance. It assists teams of data scientists, software engineers, product and business managers, in aligning their activities.
This tutorial will help you get into the right mindset to go beyond the current hype around machine learning, beyond proofs of concept, and to clearly see how this technology can have an actual impact in your domain. I’ll present the general structure of the Canvas, the different boxes it is composed of and the associated questions to answer. We’ll see how to fill it in iteratively on a churn prevention example.