I like to think of ML tasks as questions in a certain format, for which the system we're building gives answers. The question has to be about a certain "object" of the real world (which we call the input). In the supervised learning paradigm -- which we're focusing on in this series -- we would make the system learn from example objects AND from the answers for each of them. The inputs in those questions are an email and a property. The Data Sources listed in the LEARN part of the Canvas (see Part II) should provide information about these inputs.

Since the release of Osterwalder's Business Model Canvas in 2008 new canvases for specific niches have appeared. Today we have canvases for creating new gamification models, canvases for event design, for shaping a corporate culture and even for developing machine learning applications. Machine Learning Canvas is a template for designing and documenting machine learning systems. It has an advantage over a simple text document because the canvas addresses the key components of a machine learning system with simple blocks that are arranged based on their relevance to each other. This tool has become popular because it simplifies the visualization of a complex project and helps to start a structured conversation about it.

Decisions How are predictions used to make decisions that provide the proposed value to the end user? ML task Input, output to predict, type of problem. Value Propositions What are we trying to do for the end user(s) of the predictive system? What objectives are we serving? Data Sources Which raw data sources can we use (internal and external)?

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.

Machine Learning systems are complex. At their core, they ingest data in a certain format, to build models that are able to predict the future. A famous example in the industry is identifying fragile customers, who may stop being customers within a certain number of days (the "churn" problem). These predictions only become valuable when they are used to inform or to automate decisions (e.g. which promotional offers to give to which customers, to make them stay). In many organizations, there is often a disconnect between the people who are able to build accurate predictive models, and those who know how to best serve the organization's objectives.