|
--- |
|
license: apache-2.0 |
|
--- |
|
# Bylastic: A Log Classifier Compatible with Elastic |
|
|
|
## Introduction |
|
|
|
**Bylastic** is an advanced AI-based log classification model specifically designed to categorize records into three levels: **ERROR**, **WARNING**, and **INFO**. Created by **Byviz Analytics**, this model is fully optimized for integration with Elastic, offering an efficient and accurate solution for log management and analysis. |
|
|
|
## Key Features |
|
|
|
- **Accurate Classification**: Bylastic classifies logs into three critical categories: **ERROR**, **WARNING**, and **INFO**, helping to quickly identify problems, warnings, and general system information. |
|
- **Full Elastic Compatibility**: Designed to seamlessly integrate with Elastic, Bylastic facilitates data ingestion and analysis within the Elastic ecosystem. |
|
- **High Performance**: Optimized to process large volumes of logs, ensuring fast and efficient performance even in high-demand environments. |
|
- **Easy Integration**: Bylastic can be easily integrated into your existing log processing pipelines, reducing implementation time and associated costs. |
|
|
|
### Precision Bylastic vs Bert |
|
Precision in categorizing logs with example data, Bert cannot identify the categories |
|
 |
|
|
|
## Requeriments Bylastic vs Bert |
|
 |
|
|
|
|
|
## How It Works |
|
|
|
Bylastic utilizes advanced natural language processing (NLP) techniques, a branch of artificial intelligence (AI), to analyze and categorize logs. The model has been trained with a diverse set of log data, ensuring high accuracy in classification. |
|
|
|
### Log Categories |
|
|
|
- **ERROR**: Logs indicating critical failures or serious problems that require immediate attention. |
|
- **WARNING**: Logs indicating potential issues that could become errors if not properly managed. |
|
- **INFO**: Informational logs that provide details about the normal functioning of the system. |
|
|
|
## Integration with Elastic |
|
|
|
Integrating Bylastic with Elastic is straightforward and direct. Here is a quick guide to integrate the model into your Elastic environment: |
|
|
|
1. **Installation**: Download Bylastic from Hugging Face. |
|
2. **Upload the Model**: Use `eland` to upload the model to your Elastic cluster. |
|
3. **Create an inference pipeline in Elastic** |
|
|
|
## Benefits |
|
|
|
- **Improved Log Management**: Facilitates the identification and resolution of issues by automatically classifying logs. |
|
- **Time Savings**: Reduces the time required to manually review and categorize logs. |
|
- **Higher Accuracy**: Minimizes human errors in log classification. |
|
- **Easy Integration**: Seamlessly integrates with Elastic, leveraging Elastic's advanced search and analysis capabilities. |
|
|
|
|
|
# Load model directly |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForSequenceClassification |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("byviz/bylastic_classification_logs") |
|
model = AutoModelForSequenceClassification.from_pretrained("byviz/bylastic_classification_logs") |
|
``` |
|
|
|
|
|
## Test with Elastic |
|
 |
|
|
|
# Contact |
|
If you need AI models with personalized training and compatible with elastic or have any suggestions, you can contact: |
|
- **ivan.frias@byviz.com** |
|
- **ivan.frias@elastic.co** |
|
|
