Advanced Data Analysis and Processing

In the previous chapter, we explored how you can perform anomaly detection using an unsupervised learning method for timestamped data within the Elastic Stack. In this chapter, we will shift our focus to additional aspects of the Elastic Stack’s Machine Learning (ML) capabilities, such as data frame analytics, as displayed in Figure 8.1. Data frame analytics includes unsupervised learning for outlier detection, along with supervised learning methods that employ trained models for both classification and regression predictions:

Figure 8.1 – ML in the Elastic Stack

Elasticsearch’s supervised learning capabilities provide a robust framework, enabling you to train ML models with labeled training data. Once these models are trained, they can be deployed to predict outcomes or infer patterns in new datasets. This proves particularly useful when dealing with a significant amount of data and when seeking...