  Anomaly detection plays a crucial role in ensuring network robustness.
However, implementing intelligent alerting systems becomes a challenge when
considering scenarios in which anomalies can be caused by both malicious and
non-malicious events, leading to the difficulty of determining anomaly
patterns. The lack of labeled data in the computer networking domain further
exacerbates this issue, impeding the development of robust models capable of
handling real-world scenarios. To address this challenge, in this paper, we
propose an end-to-end anomaly detection model development pipeline. This
framework makes it possible to consume user feedback and enable continuous
user-centric model performance evaluation and optimization. We demonstrate the
efficacy of the framework by way of introducing and bench-marking a new
forecasting model -- named \emph{Lachesis} -- on a real-world networking
problem. Experiments have demonstrated the robustness and effectiveness of the
two proposed versions of \emph{Lachesis} compared with other models proposed in
the literature. Our findings underscore the potential for improving the
performance of data-driven products over their life cycles through a harmonized
integration of user feedback and iterative development.
