This repository is dedicated to the development and implementation of tools and techniques for log analysis and anomaly detection, similar to root cause analysis. My goal is to provide a comprehensive solution for identifying and diagnosing issues within system logs, helping to maintain the reliability and performance of various applications and systems.
The first approach is to use the concept behing of TF_ID Vectorizer. This is merely a proof of concept
-
Log Parsing and Preprocessing: Efficiently parse and preprocess log data from various sources to prepare it for analysis.
-
Anomaly Detection: Utilize advanced algorithms and techniques to detect anomalies within log data, identifying potential issues or irregularities.
-
Root Cause Analysis: Analyze anomalies to determine their root causes, providing insights into the underlying issues.
-
Visualization Tools: Generate visualizations, such as word clouds, to help interpret and communicate log analysis results.
notebook: log_anomalies_detection_tfid_vectorizer.ipynb
-
Introduce BERT and its capabilities.
-
Discuss the advantages of using pre-trained BERT embeddings, such as capturing rich, contextual information from log entries without needing extensive retraining.
-
Outline the entire workflow from log preprocessing to visualization, providing a clear guide for readers to follow.
-
Include sub-sections for each step: Log Preprocessing, Generating BERT Embeddings, - Applying DBSCAN, Preparing Data for Visualization, and Using TensorFlow Embedding Projector.
-
Visualizing Anomalies with TensorFlow Embedding Projector:
-
Explain how to use TensorFlow Embedding Projector to visualize embeddings and detect anomalies.
notebook: log_anomalies_detection_bert transformer.ipynb
-
Introduce AutoEncoders and its architecture.
-
Compare the performance between autoencoders and BERT Transformers.
notebook: log_anomalies_detection_autoencoder.ipynb
Explore more articles on Medium and follow my GitHub for AI projects.