Juliana Ferreira Alves

In many teams, KPIs are everywhere, but understanding when something is actually wrong is still a challenge. In People Analytics, for example, a sudden change in turnover, engagement, or role movement may point to a real problem, or it may simply reflect normal variation in noisy, human-centered data.

The more complex your ML project becomes, the more challenging it is to manage and deploy it into production. Beyond reproducibility, factors such as flexibility, readability, and production readiness play crucial roles in enhancing project efficiency.