How do you keep your Machine Learning communication skills sharp?

1 Know your audience

Before you start preparing your communication, you need to understand who you are talking to and what they expect from you. Different audiences have different levels of familiarity with machine learning concepts, terms, and methods, and different interests and goals in learning from you. For example, your fellow machine learning practitioners may want to know more about the technical aspects of your model, such as the data, the algorithms, the metrics, and the challenges. Your clients or stakeholders may want to know more about the business value of your model, such as the problem it solves, the benefits it brings, and the risks it mitigates. The general public may want to know more about the social impact of your model, such as the ethical, legal, and cultural implications. By knowing your audience, you can tailor your communication to their needs and preferences, and avoid confusion or boredom.

3 Simplify and visualize

Another key skill for communicating machine learning is to simplify and visualize the technical details of your model, without losing accuracy or credibility. Simplifying means using plain and simple language, avoiding jargon and acronyms, and explaining the main ideas and assumptions behind your model. Visualizing means using charts, graphs, diagrams, images, or animations to show the data, the processes, the results, or the insights of your model. By simplifying and visualizing, you can make your communication more accessible and appealing, and help your audience follow and retain your message.

4 Anticipate and address questions

A good communication is not only a one-way delivery, but also a two-way dialogue. You should anticipate and address the questions that your audience may have about your model, and invite feedback and suggestions. Anticipating questions means thinking ahead of the possible doubts, concerns, or curiosities that your audience may have, and preparing clear and concise answers. Addressing questions means listening carefully to your audience, acknowledging their perspectives, and providing honest and respectful responses. By anticipating and addressing questions, you can show your confidence and credibility, and build trust and rapport with your audience.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?