HELLO THERE!
I'm Dhieddine BARHOUMI
-Machine Learning Engineer
-MLOps Enthusiast
ABOUT ME
I'm a computer science engineering student at INSAT university, specializing in Artificial Intelligence and passionate about its incredible quick progress.
👨💻 ML & MLOps Enthusiast
📜
Google Cloud Professional ML Engineer
Certified.
📜
IBM AI Engineer
Certified.
📜
Microsoft Azure AI
Certified.
☁️ Experienced in Google Cloud Platform (GCP) and Microsoft Azure Platform.
WHAT I OFFER
As a machine learning engineer with cloud expertise, I specialize in building end-to-end ML solutions from development to deployment. I combine MLOps practices with cloud-native technologies to deliver scalable, production-ready AI systems.
SKILLS
With a knack for quick learning, I focus on mastering many skills and technologies needed for Machine Learning.
Python
Advanced
SQL
Advanced
C++
Proficient
Java
Proficient
R
Proficient
TensorFlow
Advanced
PyTorch
Advanced
Scikit-learn
Advanced
LangChain
Proficient
Hugging Face
Proficient
SpaCy
Advanced
NLTK
Advanced
Pandas
Advanced
NumPy
Advanced
OpenCV
Proficient
Git
Advanced
Docker
Advanced
Kubernetes
Advanced
Airflow
Proficient
MLflow
Proficient
Google Cloud
Advanced
Azure
Advanced
Vertex AI
Advanced
BigQuery
Advanced
Looker
Proficient
EXPERIENCE
Through diverse internships and hands-on roles, I've developed practical skills
and contributed to innovative solutions.
- Developed and maintained data pipelines for processing multi-modal sensor data, implementing ETL processes for real-time analytics.
- Engineered scalable data processing systems using Apache Spark, improving data throughput by 40% and reducing processing time.
- Implemented NLP techniques for scene understanding and text analysis, achieving 25% better accuracy in complex scenarios.
- Deployed AI models on cloud platforms (AWS, Azure) with containerized solutions using Docker and Kubernetes.
- Designed and implemented data validation and quality checks, ensuring 99.9% data integrity across all pipelines.
- Collaborated with cross-functional teams to integrate AI models into existing systems, ensuring seamless deployment and monitoring.
- Designed and implemented data pipelines for processing unstructured video and image data, optimizing storage and retrieval efficiency.
- Developed NLP-based text analysis systems for processing security logs and reports, improving information extraction accuracy by 30%.
- Implemented real-time data processing solutions using Apache Kafka and Spark Streaming for instant threat detection.
- Utilized MongoDB for efficient storage and querying of unstructured security data, reducing query time by 50%.
- Integrated AI models with existing systems using RESTful APIs and microservices architecture.
- Conducted research on multiple computer vision algorithms, choosing YOLOv8 for optimal speed and accuracy.
- Fine-tuned YOLOv8 for detecting angle and gusset objects in boxes, achieving 92.5% precision and 60% mAP50-95.
- Deployed the model as a real-time API on Microsoft Azure, integrating it with a dashboard for defect detection.
- Automated quality control, triggering alerts for misaligned or missing objects as boxes moved on a conveyor.
- Collaborated with a multidisciplinary team to replace manual defect detection for an industry client, boosting efficiency by 30%.
- Completed Microsoft Azure AI Fundamentals training and certification during the internship.
PROJECTS
Dive into a showcase of projects where I applied my technical expertise
to solve real-world challenges and create impactful solutions.
Academic Project
- Developed a scalable platform for processing and analyzing unstructured data using Apache Spark and NLP techniques.
- Implemented ETL pipelines for processing text, images, and social media content, improving data processing speed by 60%.
- Utilized BERT and SpaCy for advanced text analysis and information extraction from various data sources.
- Deployed the platform on AWS using containerized microservices architecture for improved scalability.
- Implemented data quality checks and validation processes, ensuring 99.9% data integrity.
Personal Project
- Developed a scalable machine learning application for real-time loan approval decisions.
- Designed and deployed an end-to-end pipeline including data ingestion, transformation, and model training with hyperparameter tuning and grid search.
- Implemented custom utilities, logging, and exception handling for efficient debugging and streamlined operations.
- Built a Flask-based backend with a responsive frontend using HTML and Bootstrap for user interaction.
- Automated CI/CD processes with GitHub Actions, ensuring seamless integration and deployment to Azure Web App Service.
- Dockerized the application and hosted it on Azure Container Registry for scalable cloud deployment.
Academic Project
- Designed and implemented a real-time data processing system using Apache Kafka and Spark Streaming.
- Developed data pipelines for processing streaming data from multiple sources with low latency requirements.
- Implemented data validation and quality monitoring systems using Python and Apache Spark.
- Created interactive dashboards using Tableau for real-time data visualization and monitoring.
- Integrated with MongoDB for efficient storage and retrieval of processed data.
Academic Project
- Designed a reinforcement learning-based system to navigate complex indoor environments.
- Simulated realistic home-like environments in Gazebo with dynamic obstacles.
- Leveraged ROS2 for seamless integration, utilizing RViz for real-time monitoring of the robot's path and sensor data.
- Collaborated with colleagues, employing GitHub for CI/CD, ensuring robust version control and seamless updates.
- Implemented the TD3 algorithm with a custom reward function to optimize path planning and obstacle avoidance.