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labrijisaad/README.md

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.

πŸ˜„ About Me:

Typing animation showing my roles and certifications

  • 🌱 Hello, I'm Saad, a 23-year-old based in France, with a deep passion for creating projects in the realms of Data and Artificial Intelligence.
  • πŸŽ“ I hold a Data Engineering degree from INPT.
  • πŸ’Ό Currently working as a Machine Learning Engineering Apprentice at AXA - Direct Assurance.
  • πŸ“š I'm also preparing for a Master's degree in Machine Learning and Data Science at Paris CitΓ© University.

πŸ… Certifications: (5x Azure Certified)

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

πŸ“š Contributions:

Contributed to repackaging and updating the GIT Clustering algorithm πŸ”„ based on insights from an arXiv paper, with implementation available in the GitHub repository πŸ“‚ and distribution through the TestPyPI Package πŸ“¦.

πŸ’Ό Work Experience:

  • Machine Learning Engineer / Data Scientist Apprenticeship at AXA - Direct Assurance, Paris, France (Ongoing) More details
  • Data Engineer / Data Scientist Internship at Chefclub, Paris, France (6 months) More details
  • Data Engineer Intern at Capgemini Engineering, Casablanca, Morocco (2 months)
  • Data Scientist Intern at AIOX Labs, Rabat, Morocco (2 months)
  • Web/Backend Developer Intern at DXC Technologies, Rabat, Morocco (2 months)

🌟 Top 4 Repositories

1. LLM RAG - Streamlit RAG Language Model App πŸ€–

Description: A Streamlit application leveraging a Retrieval-Augmented Generation (RAG) Language Model (LLM) πŸ€– with FAISS indexing πŸ—ƒοΈ to provide answers from uploaded markdown files. Users can upload documents πŸ“, input queries, and receive contextually relevant answers using Similarity Search πŸ”, showcasing a practical application of NLP technologies πŸ€–. The project is also equipped with a CI/CD pipeline πŸ”„ ensuring code quality & tests and simple deployment, and it supports containerization with Docker 🐳 for easy distribution and deployment.

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

2. Kedro Energy Forecasting Machine Learning Pipeline 🏯

Description: A showcase of MLOps best practices using Kedro πŸ› οΈ, this repository shows the journey of Machine Learning Models from development to deployment πŸš€, utilizing Docker 🐳. Featuring straightforward training, evaluation, and deployment of models such as XGBoost Regressor, LightGBM πŸ’‘ and Random Forest Regeressor 🌳, it integrates built-in visualization πŸ“Š and logging πŸ“ for effective monitoring. Dive into the world of modular and scalable data pipelines with Kedro πŸ“š Kedro Documentation. The integration of an automated CI pipeline πŸ”„ with Github Actions ensures code quality βœ… and reliability πŸ”’.

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

3. Repackaged GIT Clustering Algorithm 🧩

Description: An upgraded version of the GIT Clustering algorithm πŸ”„, informed by insights from an arXiv paper πŸ“„, with easy deployment via TestPyPI πŸ“¦. Includes benchmarking notebooks πŸ“Š comparing it to state-of-the-art clustering algorithms πŸ”.

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

4. Monthly & Daily Energy Forecasting Docker API ⚑

Description: This repository πŸ“¦ houses an Energy Forecasting API ⚑ that uses Machine Learning to predict daily πŸ“… and monthly πŸ—“ energy consumption from historical data πŸ“Š. 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.

πŸ™Œ Connect with Me:

LinkedIn Kaggle

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

πŸ”„ Last Updated:

  • README last updated on 17/04/2024. Regularly updated to reflect current work and interests.

Pinned Loading

  1. LLM-RAG LLM-RAG Public

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

    Jupyter Notebook 18

  2. Kedro-Energy-Forecasting-Machine-Learning-Pipeline Kedro-Energy-Forecasting-Machine-Learning-Pipeline Public

    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 7 1

  3. Git-Clustering Git-Clustering Public

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

    Jupyter Notebook 3

  4. Monthly-Daily-Energy-Forecasting-Docker-API Monthly-Daily-Energy-Forecasting-Docker-API Public

    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 3

  5. Prediction-du-cours-de-Bourse Prediction-du-cours-de-Bourse Public

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

    Jupyter Notebook 7 2

  6. Twitter-Sentiment-Analysis-with-Python Twitter-Sentiment-Analysis-with-Python Public

    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 9 1