Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring
This work addresses the problem of making ML more practical and effective for domain experts in renewable energy, but it is incremental as it builds on existing concepts like explainable ML without introducing new technical methods.
The paper tackles the challenge of deploying usable machine learning (ML) systems in real-world settings by identifying key lessons from past deployments, such as the role of 'bridges' between ML developers and domain experts, configurable systems for iteration, and continuous evaluations. It applies these lessons to wind turbine monitoring, aiming to aid decision-making for costly investigations to prevent failures, though no concrete numerical results are provided.
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.