What do you do if your machine learning ideas aren't being heard in team meetings?

If your machine learning ideas aren't getting the spotlight they deserve in team meetings, it's crucial to assess timing. Sometimes, the right moment for pitching your ideas might not align with the flow of the meeting or the priorities of the team. Consider reaching out to your team lead or scheduling a separate discussion specifically focused on your ideas. Additionally, leverage other communication channels like email or team collaboration tools to share your thoughts outside of meetings. By timing your pitches strategically and finding alternative avenues for communication, you increase the likelihood of your machine learning ideas being heard and considered.

I would assess the timing by considering factors such as the team's current workload, upcoming deadlines, and any ongoing projects that might take precedence. I'd also evaluate the level of familiarity my team has with machine learning concepts and whether there's a suitable environment for implementing such ideas. By understanding the context and timing, I can tailor my approach to present my ideas more effectively, perhaps by highlighting potential benefits or addressing any concerns that might be holding back their adoption.

I would focus on simplifying complex concepts, providing concrete examples or case studies to illustrate the potential impact, and highlighting the specific benefits for the team or project at hand. I'd also emphasize how the proposed solution aligns with the team's goals and objectives, addressing any potential concerns or misconceptions along the way. By tailoring my pitch to resonate with the interests and priorities of my colleagues, I aim to increase their understanding and buy-in, ultimately fostering a more receptive environment for exploring and implementing machine learning solutions.

Cultivate relationships with your team members and stakeholders. Building trust and rapport can make it easier for others to listen to your ideas and take them seriously.

When developing visuals that aim to explain ML, use simple terms, don't assume everyone in the room has the same level of data literacy. Most people are curious to experiment with ML, but at the same time many are cautious of the black box problem. The best way to gain buy-in from leadership is to translate it to their language.