Blindata employs automated machine learning (AutoML) techniques to analyze time series data extracted from profiling metrics for effective anomaly detection. In this chapter, we will delve into the management of models and various stages of training. The integration of AutoML with Blindata enables the system to efficiently process and interpret temporal patterns within profiling metrics, enhancing its anomaly detection capabilities.

This section will guide users through the intricacies of model handling and the comprehensive training phases, providing a detailed understanding of how Blindata utilizes machine learning to optimize anomaly detection outcomes.