  Deep Neural Networks (DNN) are nowadays largely adopted in many application
domains thanks to their human-like, or even superhuman, performance in specific
tasks. However, due to unpredictable/unconsidered operating conditions,
unexpected failures show up on field, making the performance of a DNN in
operation very different from the one estimated prior to release. In the life
cycle of DNN systems, the assessment of accuracy is typically addressed in two
ways: offline, via sampling of operational inputs, or online, via
pseudo-oracles. The former is considered more expensive due to the need for
manual labeling of the sampled inputs. The latter is automatic but less
accurate. We believe that emerging iterative industrial-strength life cycle
models for Machine Learning systems, like MLOps, offer the possibility to
leverage inputs observed in operation not only to provide faithful estimates of
a DNN accuracy, but also to improve it through remodeling/retraining actions.
We propose DAIC (DNN Assessment and Improvement Cycle), an approach which
combines ''low-cost'' online pseudo-oracles and ''high-cost'' offline sampling
techniques to estimate and improve the operational accuracy of a DNN in the
iterations of its life cycle. Preliminary results show the benefits of
combining the two approaches and integrating them in the DNN life cycle.
