Anomaly detection with TensorFlow Probability and Vertex AI

Time series anomaly detection is currently a trending topic–statisticians are scrambling to recalibrate their models for retail demand forecasting and more given the recent drastic changes in consumer behavior. As an intern, I was given the task of creating a machine-learning based solution for anomaly detection on Vertex AI to automate these laborious processes of building time series models. In this article, you will get a glimpse into the kinds of hard problems Google interns are working on, learn more about TensorFlow Probability’s Structural Time Series APIs, and learn how to run jobs on Vertex Pipelines.

Vertex Pipelines

Vertex Pipelines is Google Cloud’s MLOps solutions to help you “automate, monitor, and govern your ML systems by orchestrating your ML workflows.” More specifically, our demo runs on the open source Kubeflow Pipelines SDK that can run on services such as Vertex Pipelines, Amazon EKS, and Microsoft Azure AKS. In this article we demonstrate how to use Vertex Pipelines to automate the process of analyzing new time series data, flagging anomalies, and analyzing these results. To learn more about Vertex Pipelines, read this article.