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Before we start developing any machine learning models, we need to first motivate and design our application. While this is a technical course, this initial product design process is extremely crucial for creating great products. We'll focus on the product design aspects of our application in this lesson and the systems design aspects in the next lesson.
The template below is designed to guide machine learning product development. It involves both the product and systems design (next lesson) aspects of our application:
Product design (What & Why) → Systems design (How)
👉 Download a PDF of the ML canvas to use for your own products → ml-canvas.pdf (right click the link and hit "Save Link As...")
Motivate the need for the product and outline the objectives and impact.
Note
Each section below has a part called "Our task", which will discuss how the specific topic relates to the application that we will be building.
Set the scene for what we're trying to do through a user-centric approach:
Our task
Propose the value we can create through a product-centric approach:
Our task
We will build a platform that helps machine learning developers and researchers stay up-to-date on ML content. We'll do this by discovering and categorizing content from popular sources (Reddit, Twitter, etc.) and displaying it on our platform. For simplicity, assume that we already have a pipeline that delivers ML content from popular sources to our platform. We will just focus on developing the ML service that can correctly categorize the content.
Breakdown the product into key objectives that we want to focus on.
Our task
Describe the solution required to meet our objectives, including its:
Our task
Develop a model that can classify the content so that it can be organized by category (tag) on our platform.
Core features:
Integrations:
Alternatives:
Constraints:
Out-of-scope:
How feasible is our solution and do we have the required resources to deliver it (data, $, team, etc.)?
Our task
We have a dataset with ML content that has been labeled. We'll need to assess if it has the necessary signals to meet our objectives.
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7 | {
"id": 443,
"created_on": "2020-04-10 17:51:39",
"title": "AllenNLP Interpret",
"description": "A Framework for Explaining Predictions of NLP Models",
"tag": "natural-language-processing"
}
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Now that we've set up the product design requirements for our ML service, let's move on to the systems design requirements in the next lesson.
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To cite this content, please use:
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6 | @article{madewithml,
author = {Goku Mohandas},
title = { Product - Made With ML },
howpublished = {\url{https://madewithml.com/}},
year = {2023}
}
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