  Through past experiences deploying what we call usable ML (one step beyond
explainable ML, including both explanations and other augmenting information)
to real-world domains, we have learned three key lessons. First, many
organizations are beginning to hire people who we call ``bridges'' because they
bridge the gap between ML developers and domain experts, and these people fill
a valuable role in developing usable ML applications. Second, a configurable
system that enables easily iterating on usable ML interfaces during
collaborations with bridges is key. Finally, there is a need for continuous,
in-deployment evaluations to quantify the real-world impact of usable ML.
Throughout this paper, we apply these lessons to the task of wind turbine
monitoring, an essential task in the renewable energy domain. Turbine engineers
and data analysts must decide whether to perform costly in-person
investigations on turbines to prevent potential cases of brakepad failure, and
well-tuned usable ML interfaces can aid with this decision-making process.
Through the applications of our lessons to this task, we hope to demonstrate
the potential real-world impact of usable ML in the renewable energy domain.
