  Video analytics applications use edge compute servers for the analytics of
the videos (for bandwidth and privacy). Compressed models that are deployed on
the edge servers for inference suffer from data drift, where the live video
data diverges from the training data. Continuous learning handles data drift by
periodically retraining the models on new data. Our work addresses the
challenge of jointly supporting inference and retraining tasks on edge servers,
which requires navigating the fundamental tradeoff between the retrained
model's accuracy and the inference accuracy. Our solution Ekya balances this
tradeoff across multiple models and uses a micro-profiler to identify the
models that will benefit the most by retraining. Ekya's accuracy gain compared
to a baseline scheduler is 29% higher, and the baseline requires 4x more GPU
resources to achieve the same accuracy as Ekya.
