This image, generated with DALL-E, depicts a wide Moroccan landscape where ancient ruins and modern AI structures blend, symbolizing the harmony between the past and the future.

• 🌱 Hello, I'mSaad, a 24-year-old based in France, with a deep passion for creating projects in the realms ofData andArtificial Intelligence.
• 🎓 I hold aData Engineering degree from INPT and aMaster's degree in Machine Learning and Data Science from Paris Cité University.
• 💼 Currently working as aMachine Learning Engineering Apprentice at AXA - Direct Assurance.

• Azure Data Engineer Associate
• Azure Data Scientist Associate
• Azure Data Fundamentals
• Azure AI Fundamentals
• Azure Fundamentals

• AWS Certified Machine Learning Engineer – Associate
• AWS Certified AI Practitioner – Early Adopter

Contributed to repackaging and updating the GIT Clustering algorithm 🔄 based on insights from anarXiv paper, with implementation available in theGitHub repository 📂 and distribution through theTestPyPI Package 📦.

• Machine Learning Engineer / Data Scientist Apprenticeship atAXA - Direct Assurance, Paris, France (Ongoing)More details
• Data Engineer / Data Scientist Internship atChefclub, Paris, France (6 months)More details
• Data Engineer Intern atCapgemini Engineering, Casablanca, Morocco (2 months)
• Data Scientist Intern atAIOX Labs, Rabat, Morocco (2 months)
• Web/Backend Developer Intern atDXC Technologies, Rabat, Morocco (2 months)

• Repository:LLM RAG

Description: A Streamlit application leveraging aRetrieval-Augmented Generation (RAG) Language Model (LLM) 🤖 withFAISS indexing 🗃️ to provide answers from uploaded markdown files. Users can upload documents 📝, input queries, and receive contextually relevant answers using Similarity Search 🔍, showcasing a practical application of NLP technologies 🤖. The project is also equipped with aCI/CD pipeline 🔄 ensuringcode quality & tests and simple deployment, and it supports containerization withDocker 🐳 for easy distribution and deployment.

• Technologies/Tools: Streamlit, OpenAI API Models (LLMs, Embedding Models), FAISS, Python, Docker, CI/CD (Github Actions), Makefile, venv.

• Repository:Kedro Energy Forecasting Machine Learning Pipeline

Description: A showcase of MLOps best practices usingKedro 🛠️, this repository shows the journey ofMachine Learning Models fromdevelopment todeployment 🚀, utilizing Docker 🐳. Featuring straightforward training, evaluation, and deployment of models such asXGBoost Regressor,LightGBM 💡 andRandom Forest Regeressor 🌳, it integrates built-in visualization 📊 and logging 📝 for effective monitoring. Dive into the world of modular and scalable data pipelines with Kedro 📚Kedro Documentation. The integration of an automated CI pipeline 🔄 withGithub Actions ensures code quality ✅ and reliability 🔒.

• Technologies/Tools: Docker, Kedro, MLOps, CI/CD (Github Actions), Machine Learning (XGBoost, Random Forest, LightGBM), Jupyter Notebook, Makefile, venv, Python.

• Repository:GIT Clustering

Description: An upgraded version of the GIT Clustering algorithm 🔄, informed by insights from anarXiv paper 📄, with easy deployment viaTestPyPI 📦. Includes benchmarking notebooks 📊 comparing it to state-of-the-art clustering algorithms 🔍.

• Technologies/Tools: Benchmarking, Poetry Packaging, PyPI Distributing, Machine Learning (K-means, DBSCAN, AgglomerativeClustering, Gaussian Mixture..), Jupyter Notebook, Makefile, venv, Python.

• Repository:Monthly & Daily Energy Forecasting Docker API

Description: This repository 📦 houses an Energy Forecasting API ⚡ that uses Machine Learning to predict daily 📅 and monthly 🗓 energy consumption from historical data 📊. It's designed as a practical demonstration of a ML Engeineering/Data Science workflow, from initial analysis to a deployable API packaged with Docker 🐳.

• Technologies/Tools: MLOps, Docker, API design, Machine Learning (XGBoost, Random Forest), Jupyter Notebook, Makefile, venv, Python.

Let's make something innovative together! Feel free to reach out for collaborations or discussions in Data & Artificial Intelligence!

• README last updated on17/04/2024. Regularly updated to reflect current work and interests.

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1. LLM-RAGLLM-RAGPublic

   A Dockerized Streamlit app leveraging a RAG LLM with FAISS to offer answers from uploaded markdown files, deployed on GCP Cloud.

   Jupyter Notebook 21 2

2. Kedro-Energy-Forecasting-Machine-Learning-PipelineKedro-Energy-Forecasting-Machine-Learning-PipelinePublic

   This repo showcases a project that transforms ML model training into a simplified, production-ready Kedro Dockerized Pipeline. It emphasizes best MLOps practices, enabling easy training, evaluation…

   Jupyter Notebook 10 1

3. Git-ClusteringGit-ClusteringPublic

   Enhanced and Repackaged GIT Clustering: This repository offers an open-source, enhanced version of the GIT (Graph of Intensity Topology) clustering algorithm.

   Jupyter Notebook 4

4. Monthly-Daily-Energy-Forecasting-Docker-APIMonthly-Daily-Energy-Forecasting-Docker-APIPublic

   This repository houses an Energy Forecasting API that uses Machine Learning to predict daily and monthly energy consumption from historical data. It's designed as a practical demonstration of a Mac…

   HTML 4

5. Prediction-du-cours-de-BoursePrediction-du-cours-de-BoursePublic

   Forecast Apple stock prices using Python, machine learning, and time series analysis. Compare performance of four models for comprehensive analysis and prediction.

   Jupyter Notebook 9 2

6. Twitter-Sentiment-Analysis-with-PythonTwitter-Sentiment-Analysis-with-PythonPublic

   I aim in this project to analyze the sentiment of tweets provided from the Sentiment140 dataset by developing a machine learning sentiment analysis model involving the use of classifiers. The perfo…

   Jupyter Notebook 11 3