Tag: anomaly detection

Machine Learning, Unsupervised Learning

Donut: unsupervised anomaly detection using VAE

In this post, we are going to use Donut, an unsupervised anomaly detection algorithm based on Variational Autoencoder which can work when the data is unlabeled but can also take advantage of the occasional labels when available. In particular, we are going to focus on detecting anomalies on time series KPIs (key performance indicators) which are time-series data, measuring metrics such as the number of … Continue reading Donut: unsupervised anomaly detection using VAE

Davide LiuLeave a comment
Machine Learning, Unsupervised Learning

Log analysis for anomaly detection

Anomaly detection plays an important role in the management of modern large-scale distributed systems. Logs are widely used for anomaly detection, recording system runtime information, and errors. Traditionally, operators have to go through the logs manually with keyword searching and rule matching. The increasing scale and complexity of modern systems, however, make the volume of logs explode, which renders the infeasibility of manual inspection. To … Continue reading Log analysis for anomaly detection

Davide LiuLeave a comment