How do you communicate the results and insights of your Machine Learning models to stakeholders and clients?

3 Visualize your data and results

One of the most effective ways to communicate your ML results and insights is to use visualizations, such as charts, graphs, tables, maps, or images. Visualizations can help you convey complex and abstract information in a more intuitive and engaging way, as well as reveal patterns, trends, outliers, and anomalies that may not be obvious from numbers or text. However, you should also be careful not to overload or mislead your audience with too many or inappropriate visualizations. You should choose the right type and style of visualization for your data and results, use labels and legends to explain what they show, and use colors and shapes to highlight the key points and messages.

4 Tell a story with your data

Another way to communicate your ML results and insights is to tell a story with your data. A story can help you connect with your audience emotionally, provide a logical and coherent structure for your presentation, and persuade your audience to take action or make a decision based on your findings. To tell a story with your data, you need to have a clear beginning, middle, and end, as well as a main character, a conflict, and a resolution. For example, you can start by introducing the problem that your ML model is trying to solve, then show how your model works and what results and insights it produces, and finally conclude with the implications and recommendations for your audience.

5 Provide context and interpretation

To communicate your ML results and insights effectively, you also need to provide context and interpretation for your audience. Context means explaining how your results and insights relate to the bigger picture, such as the domain knowledge, the business objectives, the industry trends, or the social impact. Interpretation means explaining what your results and insights mean, why they matter, and what actions or decisions they suggest or support. You should also acknowledge the limitations and challenges of your ML model, such as the data quality, the model bias, the ethical issues, or the future scenarios.

6 Invite feedback and questions

The final step to communicate your ML results and insights is to invite feedback and questions from your audience. Feedback and questions can help you gauge the understanding and interest of your audience, clarify any doubts or confusion, address any concerns or objections, and improve your ML model and communication skills. You should encourage your audience to share their opinions, perspectives, and experiences, as well as ask questions that can deepen their knowledge and engagement. You should also be prepared to answer their questions honestly, respectfully, and confidently.