labrijisaad Goto Github PK

followers: 41.0 following: 15.0 repos: 47.0 gists: 0.0

Name: Labriji Saad

Type: User

Company: Machine Learning Engineer

Bio: Passionate about developing, modeling 📊, and understanding the world around us 🌍 through the lens of Data 💻 and Machine Learning 🤖

Location: Paris, France

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.

😄 About Me:

🌱 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.
🎓 I hold a Data Engineering degree from INPT.
💼 Currently working as a Machine Learning Engineering Apprentice at AXA - Direct Assurance.
📚 I'm also preparing for a Master's degree in Machine Learning and Data Science at Paris Cité University.

🏅 Certifications: (5x Azure Certified)

📚 Contributions:

Contributed to repackaging and updating the GIT Clustering algorithm 🔄 based on insights from an arXiv paper, with implementation available in the GitHub repository 📂 and distribution through the TestPyPI Package 📦.

💼 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 🤖

Repository: LLM RAG

Description: A Streamlit application leveraging a Retrieval-Augmented Generation (RAG) Language Model (LLM) 🤖 with FAISS 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 a CI/CD pipeline 🔄 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` 🏯

Repository: Kedro Energy Forecasting Machine Learning Pipeline

Description: A showcase of MLOps best practices using Kedro 🛠️, this repository shows the journey of Machine Learning Models from development to deployment 🚀, utilizing Docker 🐳. Featuring straightforward training, evaluation, and deployment of models such as XGBoost Regressor, LightGBM 💡 and Random 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 🔄 with Github 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.

3. Repackaged `GIT Clustering` Algorithm 🧩

Repository: GIT Clustering

Description: An upgraded version of the GIT Clustering algorithm 🔄, informed by insights from an arXiv paper 📄, with easy deployment via TestPyPI 📦. 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.

4. Monthly & Daily Energy Forecasting `Docker API` ⚡

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.

🙌 Connect with Me:

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

🔄 Last Updated:

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

Labriji Saad's Projects

analyzing-and-forecasting-a-time-series-with-python

The main objective of this project was to study a time series to approach and manipulate abstract notions that we have seen during the course on time series class. Among the things we did in this project: the decomposition of the series, the study of each component, the stationarity test, the choice of the degree of the model, the learning of the latter and the forecast of future values and much more...

apache-beam-k-means

Implementing K-means clustering in sequential, streaming, and distributed formats using Apache Beam.

axa-direct-ml-apprenticeship

Repository showcasing my Machine Learning Engineering Apprenticeship at AXA-Direct Assurance, contributing to the development and implementation of Machine Learning solutions.

car-price-prediction

Car Price Prediction

car_renting_application_with_spring-boot

The car rental management system can be used by companies that manage a car rental project, note that this project only runs on a local machine, but can be modified to run on multiple machines simultaneously for more high efficiency and to serve the whole company.

chefclub-data-internship

Repository showcasing my Data Engineer / Scientist internship at Chefclub, contributing to data infrastructure enhancement and fostering data-driven insights.

clusterllm

LLM guided text clustering

data-scientist-tools-matplotlib

In this hands-on training notebook how to use the matplotlib library to plot beautiful graphs in python.

data-scientist-tools-pandas

In this hands-on training notebook, we'll see all the basics of the Pandas library used for data science/data analysis and machine learning tasks.

data-warehousing-in-azure-postgresql

In this repository, I address missing values in the Prosper dataset using advanced data cleaning techniques. The refined data is then seamlessly uploaded to a pre-configured Azure Postgres Database via a Jupyter Notebook, showcasing efficient data management and cloud database integration.

deepmoji

State-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc.

devenvconfigurations

🚀 Centralized repository for my customized IDE settings, system configurations, and tech stack preferences. 🛠️

dfinder_console_mode_with_java

Dfinder is a local file browser, which searches through files (according to the user's choice) and then generates a txt file containing the search results.

diabetes-prediction-using-machine-learning

In this example of classification problem, we will try to find the best model for our dataset to predict diabetes using machine learning in Python.

dimension-reduction-clustering-data

explore the impact of dimensionality reduction on the quality of clustering.

dimreduce-healthanalytics

A project showcasing the application of various dimensionality reduction techniques for visualizing and analyzing simulated health diagnostics data in 2D and 3D.

examen-data-engineering

In-class exams for Docker, Git, and ML.

exploratory-data-analysis-in-python

In this project, we will see in a hands-on training jupyter notebook how to effectively diagnose and deal with missing data in Python.

few-shot-clustering

first-android-app

Here is the source code for our first inclass Android app.

git

density growing clustering

git-clustering

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

gradient-descent-algorithm-with-python

in this project we'll Implement the basic functions of the Gradient-Descent algorithm to find the boundary in a small dataset.

itilv4

kedro-energy-forecasting-machine-learning-pipeline

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, and deployment of models, including XGBoost, LightGBM and Random Forest, with built-in visualization and logging features for effective monitoring.

labrijisaad Goto Github PK

😄 About Me:

🏅 Certifications: (5x Azure Certified)

📚 Contributions:

💼 Work Experience:

🌟 Top 4 Repositories

1. LLM RAG - Streamlit RAG Language Model App 🤖

2. Kedro Energy Forecasting Machine Learning Pipeline 🏯

3. Repackaged GIT Clustering Algorithm 🧩

4. Monthly & Daily Energy Forecasting Docker API ⚡