• Understanding machine learning, including the basics of how it works and the different types of it.
  • Collaborating with data scientists, understanding their process, and facilitating collaboration as a product designer.
  • Translating algorithm outputs to create user experiences, and the different considerations and tradeoffs to be aware of.

Understanding machine learning

First off and I guess the most obvious thing to learn is the basics of how machine learning works. I highly recommend a course by Andrew Ng founder of deeplearning.ai called ‘AI for everyone’ — It’s very comprehensive, easy to watch, and will give you all the context you need to get started. Also, it’s worth keeping this glossary at hand in case any of the terms don’t make sense.

Graphic illustration of a diagram showing machine learning proccess

As you can see from above, a key part of any machine learning model is the data that powers it. As a designer, it’s important to understand what types of data are out there and how data gets sourced and used. This should give you enough context to be involved in conversations around data and watch out for bias in the model. Bias in AI is a hot topic and I’m sure there are many articles about it, however, one thing to call out is that typically bias happens from poor or incomplete data that amplifies biases in the real world.

Finally it’s worth learning about concepts related to machine learning model outputs as you will be using these outputs to create experiences for users. Particularly concepts like confusion matrix, accuracy, explainability, precision, and recall. The impact of these concepts will be covered in the last part of this series, but it’s good to familiarise beforehand.

Collaborating with data scientist

Having foundation knowledge of how machine learning works will definitely propel you towards success, however in my opinion knowing how to effectively collaborate with data scientists is one of the most important skills for a product designer when working on machine learning-driven products or features.

Graphic illustration of a diagram showing machine learning process

Translating algorithm output to create experiences

Final skill to highlight is the ability to translate machine learning algorithm output into user experiences. Translate is a useful word here because, just like languages, understanding one is necessary in order to construct a sentence in another. Similarly, in order to provide a seamless experience for users, you need to understand how to use the outputs of machine learning algorithms. Understanding the fundamentals of how machine learning works, as well as working collaboratively with data scientists, will definitely help in understanding the output — however, translating it into something intuitive and easy to understand for users is a skill on its own.