A new approach—applying vectors and embeddings to log analytics

As you learned in Chapter 1, Introduction to Vectors and Embeddings, vectors and embeddings can be used to describe all sorts of notions and concepts, and we can build vector spaces that represent a given domain. When it comes to vectorizing logs, a couple of approaches are available. In the following sections, we will review two approaches that we believe are the most common ones: training or fine-tuning an existing model for logs and generating human-understandable descriptions and vectorizing these descriptions.

Approach 1—training or fine-tuning an existing model for logs

In this approach, an existing language model, such as word2vec, BERT, or GPT, is trained or fine-tuned specifically for log data. This means using a dataset of raw logs to adapt the model’s parameters so that it can effectively capture the unique characteristics and patterns found in log data. The trained or fine-tuned model...