From Data to AI with the Machine Learning Canvas (Part I)

A framework to connect the dots between data collection, machine learning, and value creation

Louis Dorard
Nov 18, 2016 · 5 min read
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Canvases are very popular in the startup community, starting with the hugely popular Lean Canvas, which is itself derived from the Business Model Canvas. It provides an overview of this complex object that a Business Model is, and facilitates collaboration.

  • How are we using predictions powered by that learning
  • How are we making sure that the whole thing “works” through time?
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Introducing the Machine Learning Canvas

The Machine Learning Canvas allows to describe precisely this. It starts with a central block dedicated to the Value Proposition of the system where ML is going to be used. You can think of it as the What+Why+Who: What are we trying to do, Why is it important, and Who is going to use the system / be impacted by it. Then there’s the How, which can be split in two parts: learning and making predictions.

  • Decisions: How are predictions used to make decisions that provide the proposed value to the end­ user?
  • Making predictions: When do we make predictions on new inputs and how long do we have for that?
  • Offline evaluation: Which methods and metrics can we use to evaluate the way predictions are going to be made and used, prior to deployment?
  • Collecting data: How do we get new data to learn from (inputs AND outputs)?
  • Features: Input representations to extract from raw data sources.
  • Building models: When do we create/update models with new training data and how long do we have for that?

Using the canvas in your work

The ML Canvas lets you lay down your vision for your ML system and communicate it with your team. It’s a first step to make sure you connect what ML can do to your organization’s objectives, and towards assessing feasibility.

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ML⚡️DL — AI to Hell!

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