M2-Spam-Detector-AI is an advanced spam detection system leveraging Transformer-based models and ensemble machine learning techniques. This project aims to provide a scalable, accurate, and deployable solution for detecting spam in text datasets, emails, or messaging platforms.
- Author: Md Mahbubur Rahman
- GitHub: https://github.com/m-a-h-b-u-b
- Website/Portfolio: https://m-a-h-b-u-b.github.io
- Transformer-based NLP models for high accuracy spam detection.
- Ensemble learning for improved prediction performance.
- Modular, well-organized architecture for easy maintenance.
- Dockerized setup for quick deployment.
- Kubernetes configuration for scalable cloud deployment.
- Jupyter notebooks for experimentation and analysis.
- Programming Language: Python 3.x
- NLP Frameworks: Hugging Face Transformers (BERT, RoBERTa, etc.)
- Machine Learning: scikit-learn, XGBoost, TensorFlow / PyTorch
- Data Processing: pandas, NumPy, NLTK, spaCy
- Web/API Frameworks: Flask, FastAPI (for serving predictions)
- Containerization: Docker
- Orchestration: Kubernetes
- Version Control: Git / GitHub
- Testing: pytest
- Visualization & Notebooks: Jupyter, matplotlib, seaborn
- Deployment & CI/CD: GitHub Actions, Docker Hub
+----------------------+
| Input Text Data |
+----------+-----------+
|
v
+----------------------+
| Data Cleaning & NLP |
| - Tokenization |
| - Normalization |
| - Stopword Removal |
+----------+-----------+
|
v
+----------------------+
| Transformer Encoder |
| (BERT, RoBERTa, etc.)|
+----------+-----------+
|
v
+----------------------+
| Feature Engineering |
| - TF-IDF / Embeddings|
| - Statistical Features|
+----------+-----------+
|
v
+----------------------+
| Ensemble Classifier |
| - Random Forest |
| - XGBoost |
| - Neural Networks |
+----------+-----------+
|
v
+----------------------+
| Predictions & API |
| - REST/Flask/FastAPI |
| - Batch/Streaming |
+----------------------+
The M2-Spam-Detector-AI is designed to run on cloud platforms for high availability, scalability, and production readiness.
AWS / GCP Cloud Setup:
-
Compute & Orchestration:
- Dockerized services deployed on EC2 (AWS) or GKE (GCP Kubernetes) clusters.
- Kubernetes handles scaling via Horizontal Pod Autoscaler.
- Load balancing through Application Load Balancer (ALB) or GCP Load Balancer.
-
Storage & Databases:
- RDS / Cloud SQL for structured relational data (messages, logs).
- S3 / Cloud Storage for model artifacts, datasets, and backups.
- Optional NoSQL database (DynamoDB / Firestore) for high-speed key-value access.
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Serverless & Async Tasks:
- AWS Lambda / Cloud Functions for background batch processing, cleanup jobs, and asynchronous spam prediction tasks.
-
CI/CD & Monitoring:
- Automated pipelines via GitHub Actions: build Docker images, push to Docker Hub / Container Registry, deploy to Kubernetes.
- Monitoring and alerting with CloudWatch, Stackdriver, and Prometheus + Grafana for metrics, logs, and system health.
Benefits of Cloud Deployment:
- Auto-scaling to handle spikes in traffic.
- Fault tolerance with multi-AZ (Availability Zone) deployments.
- Centralized logging and monitoring for operational efficiency.
- Simplified experimentation and rapid deployment of updated ML models.
This project is dual-licensed:
- Open-Source / Personal Use: Apache 2.0
- Commercial / Closed-Source Use: Proprietary license required
For commercial licensing inquiries or enterprise use, please contact: [email protected]