When working in a diverse team, it is important to understand the background and roles of each member in order to avoid miscommunications and misunderstandings. In some cases, different team members may fail to agree on the true objective, or misinterpret it altogether.
For example, we may want to develop a recommendation model that only uses data from the last 15 days, but fail to clearly communicate this constraint within the team.
Sharing a clearly defined objective within the team assumes the training objective can be converged towards each member, using specific disciplinary language and terminology.
This practice ensures that effort is not spent on futile activities and enhances team communication and efficiency.
Moreover, it facilitates alignment with the team’s goal, and ensures that the outcomes of training can be correctly evaluated.
- How do teams work together on automated ML projects
- Managing Machine Learning Projects
- Rules of Machine Learning: Best Practices for ML Engineering