Wissam Hmam

Design and development of an intelligent descriptive and predictive analytics platform for WebLogic server incidents.
Technologies: Python, Pandas, Scikit-learn, XGBoost, Sentence-Transformers, LLaMA, Transformers, spaCy, NLTK, Docker, Git
• Designed an NLP pipeline that automated WebLogic log analysis, significantly reducing manual incident investigation efforts.
Developed a semantic similarity engine to automatically identify related incidents and accelerate troubleshooting.
• Trained and optimized XBoost models to predict incident categories and anticipate potential system failures.
• Integrated LLaMA 3 to automatically generate technical summaries and root cause analysis suggestions.
• Designed a hybrid Al approach combining business rules, Machine Learning, and Generative Al to improve analysis reliability.