Introducing the AI Project Canvas

⬜️AI Project CanvasImagine the following scenario: You have a brilliant idea for a new AI project.

To make it happen, you need to convince management to fund your idea.

You need to pitch your AI project idea to stakeholders and management.

Yuck.

This is the first step where the AI Project Canvas comes into play.

Although Louis Dorard has already created the ML Canvas, the AI Project Canvas focuses on explaining the business value of your AI project.

The AI Project Canvas helps you to structure and convey the holistic idea of your AI project to others.

The AI Project Canvas consists of four distinct parts: the Value Proposition as the central part of your project, the Ingredients on the left, the integration for Customers on the right, and Financing on the bottom.

All these parts are vital aspects of any AI project.

Let’s go through each part and start with the Value Proposition.

The heart of the AI Project Canvas is the Value Proposition.

It explains the value that the project will add to your organization.

A value-add can be a new AI-powered product to generate revenue, or improving an existing process to cut costs.

What customer pain is the AI project solving?.Which vitamins are you adding to enhance your customer’s life?.Ideally, you can describe the Value Proposition in one concise bullet point.

Listing many Value Propositions risks watering down the impact or failing to focus on the most important one.

Side note: Trying out a new paper because it sounds cool will not get you far.

What is the value-add of your AI project?.Sufficiently answering this question help you focus on the task at hand and get you halfway towards securing funding for your AI project.

Let’s look at the Ingredients block next.

The Ingredients part consist of the Data, Skills, and Output blocks.

Data is the main element that every AI project relies on.

The better you can explain what data you need to create the value proposition, the better for your AI project.

How much data do you need?.Do you have already a prepared dataset or do you need to source it?.Does it have to be labeled?.What data format are you expecting?In the Skills block, you will define the expertise you need.

Is it a computer vision or natural language understanding task?.Do you need Data Engineers to help you write efficient software?.Maybe even a Product Manager and a UX Designer to gather customer requirements and to design a workflow?The Output block shows the single key metric you’re evaluating on.

Andrew Ng recommends in his book Machine Learning Yearning chapter 8 to define a single-number evaluation metric before starting the project.

This helps you choose a good model in the first place and then to compare the performance of different models based on this metric.

Output metrics could be accuracy, f1-score, precision or recall, minutes spent using the service, etc.

The output metric could be supplemented with a sufficing metric, e.

g.

that accuracy has to exceed 95% (key metric) while taking no longer than 1s inference time (sufficing metric).

After explaining the Ingredients part of your AI project, let’s talk about how you will bring your AI project to the Customer next.

The right part of the AI Project Canvas covers the integration of your project into the current infrastructure, for stakeholders and the customer.

AI products rarely live in an isolated world, hardly ever in a Jupyter Notebook.

They always have to be integrated into an existing architecture.

Explain where and how the project will be used.

Where does it fit into the backend?.How will the customer engage with your model?.Will you use a microservice, monolith, or predict on-the-fly during streaming?.Answering these questions will make it clear how the project will be brought into production.

Listing the Key Stakeholders will give you an overview of important decision makers.

Key Stakeholders can be internal departments like legal, UX, management or even external stakeholders like contractors, owners, political or non-profit groups.

The right-most block is the second most important block after the Value Proposition.

Who is the Customer that you are designing the project for?. More details

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