  We discuss how VMware is solving the following challenges to harness data to
operate our ML-based anomaly detection system to detect performance issues in
our Software Defined Data Center (SDDC) enterprise deployments: (i) label
scarcity and label bias due to heavy dependency on unscalable human annotators,
and (ii) data drifts due to ever-changing workload patterns, software stack and
underlying hardware. Our anomaly detection system has been deployed in
production for many years and has successfully detected numerous major
performance issues. We demonstrate that by addressing these data challenges, we
not only improve the accuracy of our performance anomaly detection model by
30%, but also ensure that the model performance to never degrade over time.
