AnomalyNodeML

This service focuses on server log data to detect anomalies and predict them accurately using a TensorFlow model.

Steps to Build the Model:

1. Data Processing:
   • Data Cleaning
   • Removing Unnecessary Columns
   • Transforming Categorical Data using Label Encoder
2. Model Building:
   • A Sequential TensorFlow Model
3. Model Evaluation:
   • Evaluate the Model on Test Data

The model can be trained with multiple training datasets and evaluated against evaluation data.

Running the Service Locally Using Docker:

1. Build the Docker image and run the service:

   docker-compose build --no-cache
   docker-compose up -d

2. To stop and remove the service:

   docker-compose down