Embedded/edge ML folks: what actually eats the most time ,getting data, or cleaning/labeling it (time series sensor data, not computer vision/audio)? [D]
A Reddit user on r/MachineLearning is seeking to identify the primary time sink for developers working with embedded/edge machine learning, specifically for time-series sensor data. The user is developing a hardware-agnostic, AI-native platform for time-series data, aiming to alleviate common development bottlenecks. They are soliciting community input on whether data acquisition, cleaning/labeling, model training, or deployment optimization consumes the most developer time. AI
IMPACT Developers in edge ML are debating whether data acquisition or data cleaning/labeling presents the biggest challenge.