RIDA BAYI

🎯 Objective: Deploy reliable and scalable AI solutions in production by designing automated Data & MLOps pipelines in a highly secure and demanding environment. 🚀 Key achievements: - End-to-end AI pipeline design: data ingestion, processing, training, experiment monitoring, and deployment - Implementation of a complete CI/CD chain with GitLab, integrated with MLflow and DVC - Containerization with Docker to ensure portability, reproducibility, and continuous integration - Model deployment via internal REST APIs in a regulated environment - Python script optimization: 25% execution time reduction - Creation of modular and scalable technical documentation to facilitate the scalability of solutions on other projects 🧠 Teck-Stack: Python, Docker, GitLab CI/CD, MLflow, DVC, TensorFlow, Pandas, REST APIs, Linux, Bash

🎯 Objective: Develop an AI-based decision support solution to streamline patient flow management in hospital emergency departments. 📌 Key Contributions: - Design of a deep learning NLP model (LSTM) to predict admission severity levels in real time - Exploitation of multimodal data from electronic medical records (EMR): * Vital signs (blood pressure, temperature, SPO2, heart rate) * Clinical text notes (physicians, nurses) * ICD codes (diagnosis classification) - Preprocessing, cleaning, and vectorization of heterogeneous data while adhering to security and privacy standards - Improving model performance through rebalancing, tuning, and cross-validation strategies - Deployment of a real-time Power BI dashboard to visualize admission prioritization and facilitate medical decisions 🛠️ Stack: Python, NLP, TensorFlow, LSTM, Pandas, Power BI, SQL, Scikit-learn, Matplotlib