Wassim Seifeddine, Developer in Paris, France
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Wassim Seifeddine

Verified Expert  in Engineering

Machine Learning Developer

Location
Paris, France
Toptal Member Since
September 23, 2022

Wassim is a software engineer with 7+ years of experience, including 4+ years in machine learning. He worked with various clients, from startups to research institutes to multinational corporations. Wassim stands out from the crowd because he is thorough about building scalable solutions that are adaptive to business requirements.

Portfolio

Animaj
PyTorch, Data Lakes, Data Warehousing, Trend Forecasting, Topic Modeling...
SAFE SIGN TECHNOLOGIES LIMITED
Machine Learning, Large Language Models (LLMs), Fine-tuning, Together.ai...
Mawdoo3 Ltd
Artificial Intelligence (AI), Llama 2, Natural Language Processing (NLP)...

Experience

Availability

Part-time

Preferred Environment

PyTorch, PySpark, NumPy, Jupyter Notebook, Pandas, Amazon Web Services (AWS), Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Computer Vision, Python, Artificial Intelligence (AI), GPU Computing

The most amazing...

...project I’ve built is a legal case classification model, a system that classifies case descriptions and highlights crucial text elements.

Work Experience

Machine Learning Engineer

2022 - PRESENT
Animaj
  • Cultivated a concept extraction from text, audio, and video content to understand social media trends better. Concepts include emotions, activities, and sentiments on what's going on in the video.
  • Developed a set of Apache Airflow Directed Acyclic Graphs (DAGs) to orchestrate data and machine learning (ML) pipelines.
  • Created a monitoring system to have a bird's-eye view of the whole system with outlier detection for out-of-distribution inputs/outputs.
  • Fine-tuned LLMS (gptj, gpt-neox, llama) on new datasets using memory and time-efficient techniques like LoRA, DeepSpeed ZeRO, and PyTorch's FSDP.
  • Worked on 2D/3D image/video generation models using Stable Diffusion and other generative AI models fine-tuned on a custom dataset.
  • Experienced (research and industry) on quantized models for more efficient training and inference.
  • Worked on cutting-edge image models for segmentation and objection detection.
  • Built a model that helps understand how videos are trending using unsupervised learning to cluster similar videos together.
  • Implemented a YouTube ad optimization model for optimal placement of mid-roll ads.
Technologies: PyTorch, Data Lakes, Data Warehousing, Trend Forecasting, Topic Modeling, Revenue Projections, Video Analysis, Speech Synthesis, Sound, Text Animation, Diffusion Models, Image Generation, Databricks, Matplotlib, Predictive Modeling, PySpark, NumPy, Deep Neural Networks, Data Engineering, AI Design, Forecasting, Data Visualization, Algorithms, Data Analytics, OpenAI, OpenCV, Computer Vision Algorithms, Language Models, OCR, Consulting, Startup Consulting, ETL, Apache Airflow, Data Analysis, Predictive Analytics, SQL, GPT Neo, Google Publisher Tag (GPT), Statistics, Python 3, Datasets, Causal Inference, Fine-tuning, Data Inference, Text Generation, Scikit-learn, Programming, Amazon Web Services (AWS), Speech Recognition, Architecture, Web Development, Snowflake, Analytics, AWS Lambda, NoSQL, Amazon EKS, Bash, Docker, MySQL, XGBoost, Large Language Models (LLMs), AWS ELB, Amazon Elastic Container Service (Amazon ECS), AWS Fargate, API Integration, Generative Pre-trained Transformer 4 (GPT-4), OpenAI GPT-3 API, Data Management, YouTube API, YouTube Ads, Optimization, Videos, Stable Diffusion, ControlNet, LoRa, Distributed Computing, Amazon DynamoDB, Apache Spark, Spark, Generative Pre-trained Transformer 3 (GPT-3), OpenAI GPT-4 API, Software Architecture, Graphics Processing Unit (GPU), Convolutional Neural Networks (CNN), Image Analysis, Machine Learning Operations (MLOps), GPU Computing, Cloud Architecture, Models, Generative Artificial Intelligence (GenAI), Generative AI, Object Detection, JavaScript, Prompt Engineering, AI-generated Video

Machine Learning Engineer (via Toptal)

2024 - 2024
SAFE SIGN TECHNOLOGIES LIMITED
  • Implemented custom loss functions for LLM fine-tuning.
  • Implemented continued pre-training and instruction fine-tuning scripts for LLMs on multi-node clusters.
  • Ensured dataset consistency and proper formatting of instruction tokens.
Technologies: Machine Learning, Large Language Models (LLMs), Fine-tuning, Together.ai, Distributed Cloud, PyTorch, AI Model Training, Instruction Tunning

Machine Learning Engineer

2023 - 2024
Mawdoo3 Ltd
  • Developed and optimized a fine-tuning pipeline for the LLAMA 2 model, enhancing its adaptability for diverse new domains and languages.
  • Oversaw the development of the distributed training pipeline to run on GCP Vertex AI.
  • Implemented comprehensive monitoring systems for training and inference.
Technologies: Artificial Intelligence (AI), Llama 2, Natural Language Processing (NLP), Google Cloud Platform (GCP), Machine Learning Operations (MLOps), Prompt Engineering, Retrieval-augmented Generation (RAG), Instruction Tunning

AI/ML Expert

2023 - 2023
Sneaky Panda Limited
  • Developed a neural network to mimic real players in our game, serving as a tool for balancing the game's economy.
  • Advised the client on architectural design and recommended subsequent steps to be taken.
  • Created an AI model that simulates player behavior for the purpose of adjusting and optimizing the game's economic system.
Technologies: Reinforcement Learning, Deep Reinforcement Learning, AI Programming, Artificial Intelligence (AI), Machine Learning, Game Development, Mobile Games, DQN, Data, Instruction Tunning

PyTorch Deployment Specialist – Product Auditing

2023 - 2023
OctoML, Inc.
  • Conducted a comprehensive audit of the AI platform, identifying key areas for performance enhancement and user experience improvements.
  • Facilitated user experience improvements by highlighting areas for enhancing the new user experience, influencing a redesign that increased user engagement and satisfaction.
  • Documented all audit findings and recommendations, offering a clear and actionable guide for platform enhancements.
Technologies: PyTorch, Python, Machine Learning, Instruction Tunning

NLP Engineer

2023 - 2023
Manada Technology LLC
  • Acted as the AI advisor for building personalized GPT models on custom datasets.
  • Helped optimize data ingestion and cleaning pipelines.
  • Applied techniques from prompt engineering to improve the LLM generation quality.
  • Assisted the development team in optimizing their development and experimentation process.
Technologies: Machine Learning, Artificial Intelligence (AI), Natural Language Processing (NLP), Large Language Models (LLMs), Data Engineering, Generative Pre-trained Transformers (GPT), Modeling, Fine-tuning, LLaMA, MosaicML, OpenAI, OpenAI GPT-4 API, Prompt Engineering, Retrieval-augmented Generation (RAG), Instruction Tunning

NLP/Python Developer

2023 - 2023
Northeastern University - College of Engineering
  • Worked on a tool to implement aspect-based sentiment analysis on product reviews.
  • Developed a mechanism to summarize reviews based on aspects.
  • Fine-tuned ML models on custom datasets to improve performance.
Technologies: Natural Language Processing (NLP), Machine Learning, Python, Amazon Web Services (AWS), APIs, JavaScript, Prompt Engineering, Retrieval-augmented Generation (RAG), Instruction Tunning

AI/ML Expert

2023 - 2023
1Bstories
  • Developed a system to generate realistic-looking avatars based on multiple user choices to use with TTS models.
  • Built a scene generation pipeline that generates images based on a specific description and preference.
  • Deployed MLflow to production to monitor all ongoing machine learning model inferences.
Technologies: Artificial Intelligence (AI), Machine Learning, Stable Diffusion, Text to Image, Text to Video, MLflow, Midjourney, Avatars, Image Generation, Models, JavaScript, Prompt Engineering, Retrieval-augmented Generation (RAG), Instruction Tunning

AI/ML Specialist

2023 - 2023
Rich Lemon Apps FZE LLC
  • Trained the AI model to generate user avatars in a specific style while preserving facial features.
  • Achieved significant improvements compared to the previous approach using Dreambooth, resulting in avatars that closely resemble the original faces.
  • Applied specific prompt formatting to improve the relevance of the generated avatars with the client's requirement.
  • Utilized strong knowledge of AI/ML, Python, LoRA, diffusion models, stable diffusion, and image processing to accomplish project goals.
Technologies: Artificial Intelligence (AI), Machine Learning, Stable Diffusion, Image Processing, Fine-tuning, Python, ControlNet, LoRa, DreamBooth, Kohya, Convolutional Neural Networks (CNN), Image Analysis, GPU Computing, Models, Prompt Engineering, Instruction Tunning

Machine Learning Engineer

2023 - 2023
Martian Learning Inc.
  • Helped with debugging evaluation issues for custom deep learning model.
  • Worked with a team of AI researchers to debug the performance of the model router.
  • Ran the model on a GPU cluster for training and evaluation.
Technologies: Machine Learning, Artificial Intelligence (AI), Python, PyTorch, Distributed Computing, Machine Learning Operations (MLOps), GPU Computing, Models, Instruction Tunning

Machine Learning Engineer

2023 - 2023
Odem Global Pty Ltd
  • Fine-tuned LLMs to deploy to a decentralized blockchain.
  • Developed a gRPC server to offload inference to remote servers.
  • Improved the inference speed of cutting-edge LLMs to deploy on more resource-constrained servers.
  • Applied prompt engineering and network (data flow) analysis to ensure the LLMs generated the best possible responses among the top 1,024 competing models.
Technologies: Language Models, Machine Learning, Python, PyTorch, Fine-tuning, Causal Inference, Flash Attention, OpenAI GPT-3 API, APIs, Graphics Processing Unit (GPU), Convolutional Neural Networks (CNN), Image Analysis, Machine Learning Operations (MLOps), GPU Computing, Cloud Architecture, Project Management, Models, JavaScript, Prompt Engineering, Retrieval-augmented Generation (RAG), Instruction Tunning

AI Developer

2023 - 2023
CodeComplete, Inc
  • Engaged in the optimization of a causal language model's acceleration.
  • Explored novel concepts to enhance performance by leveraging state-of-the-art technologies in the field.
  • Conducted in-depth analysis and experimentation to identify potential bottlenecks and develop innovative solutions for optimizing the performance of the language model.
  • Stayed abreast of the latest advancements in the field of language modeling and explored their applicability to improve further the acceleration and overall efficiency of the causal language model.
Technologies: C++, Machine Learning, Artificial Intelligence (AI), NVIDIA CUDA, PyTorch, cuBLAS, Flash Attention, gRPC, Generative Pre-trained Transformers (GPT), Generative Pre-trained Transformer 3 (GPT-3), Software Architecture, Graphics Processing Unit (GPU), Machine Learning Operations (MLOps), Cloud Architecture, Models, Prompt Engineering, Instruction Tunning

OCR AI Developer

2023 - 2023
ACFT PERFO
  • Implemented a solution using AWS Textract to extract structured data from PDF files.
  • Developed an end-to-end pipeline for extracting and saving the data into a data lake.
  • Provided alternative solutions and tools to improve the efficiency and accuracy of the data extraction process.
Technologies: Amazon Textract, Amazon Web Services (AWS), Amazon SageMaker, OCR, Artificial Intelligence (AI), Python, Machine Learning, Amazon S3 (AWS S3), Python 3, Consulting, Software Architecture, Convolutional Neural Networks (CNN), Image Analysis, Project Management, Models, JavaScript

Senior AI | Tech | HR Consultant

2023 - 2023
Block Born LLC
  • Advised on AI tools to generate creative suggestions for game content production based on schema and scale.
  • Reviewed and provided feedback on ideas related to implementing an AI tool for creative content suggestions.
  • Demonstrated deep expertise in AI and its applications for creative content generation.
  • Designed a specific prompt template to get the output of the LLM in the desired format that passes a validation pipeline.
Technologies: Artificial Intelligence (AI), Consulting, ChatGPT, Plugins, Generative Pre-trained Transformer 4 (GPT-4), APIs, OpenAI GPT-3 API, Stable Diffusion, ControlNet, Generative Pre-trained Transformer 3 (GPT-3), OpenAI GPT-4 API, Convolutional Neural Networks (CNN), Image Analysis, Machine Learning Operations (MLOps), Cloud Architecture, Project Management, Models, JavaScript, Prompt Engineering

Machine Learning Engineer

2023 - 2023
Advest Marketing, LLC
  • Worked on optimizing a product ads algorithm for social media.
  • Researched techniques for improving product placement in videos.
  • Explored deep learning techniques for generative modeling for videos.
Technologies: Machine Learning, Python, PyTorch, TensorFlow, Deep Learning, Diffusion Models, Generative Models, Graphics Processing Unit (GPU), Convolutional Neural Networks (CNN), Image Analysis, Machine Learning Operations (MLOps), Cloud Architecture, Project Management, Models

ML Engineer with Skills in GPT-2/3

2022 - 2023
Toptal
  • Trained a GPT-style model for simple language modeling to be deployed on a bit tensor network.
  • Configured an iterative procedure to train models from previous checkpoints on new datasets.
  • Monitored the model performance during and after training to know when to retrain.
Technologies: Machine Learning, Deep Learning, Python, Text Generation, Language Models, Blockchain, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Distributed Computing, Generative Pre-trained Transformer 3 (GPT-3), OpenAI GPT-4 API, Software Architecture, Graphics Processing Unit (GPU), Machine Learning Operations (MLOps), Cloud Architecture, Project Management, Models, Prompt Engineering, Instruction Tunning

Lead Machine Learning Engineer

2021 - 2023
Quantum Analytica
  • Worked as a startup consultant for a real-estate startup to help them better understand their data infrastructure and guide them to the right tools using ETL pipelines, data lakes, delta tables, and hot storage.
  • Developed a complete set of PySpark ETLs for transforming, cleaning, and normalizing data from different data sources and industries, including real estate and agriculture.
  • Built a personalized ML-powered employee reward model as an MVP for an early-stage startup using customer-level data and rewards data from different providers.
  • Managed the tech team, including designing the architecture for the whole team. The architecture ranges from web scrapers with dynamic proxies until the data is in hot storage, ready to be used by REST APIs.
  • Developed machine learning models for consumer demand forecasters in the retail domain focusing on optimizing distribution to avoid out-of-stock.
  • Worked on an algorithm for forecasting consumer demand for a certain set of products to help our client engage strategically in new markets.
  • Developed an price outlier detection system for real estate ecommerce websites to notify users of potential good deals.
Technologies: PyTorch, ETL, PySpark, REST APIs, NumPy, Jupyter Notebook, Pandas, Machine Learning, Computer Vision, Python, Artificial Intelligence (AI), Recommendation Systems, Data Science, Image Processing, Advisory, Databricks, Deep Neural Networks, Matplotlib, Data Engineering, AI Design, Forecasting, Data Visualization, APIs, Algorithms, Data Analytics, OpenAI, Predictive Modeling, OpenCV, Computer Vision Algorithms, Topic Modeling, Trend Forecasting, Data Warehousing, Data Lakes, Language Models, OCR, Data Scraping, Consulting, Technology Consulting, Slurm Workload Manager, Startup Consulting, Apache Airflow, Data Analysis, CTO, Predictive Analytics, SQL, GPT Neo, Google Publisher Tag (GPT), Statistics, Python 3, Datasets, DeepSpeed, Causal Inference, Fine-tuning, Data Inference, Text Generation, Scikit-learn, Programming, Amazon Web Services (AWS), Quantitative Finance, Speech Recognition, Architecture, Snowflake, Analytics, NoSQL, Amazon EKS, Bash, Docker, MySQL, XGBoost, Image Recognition, Handwriting Recognition, Large Language Models (LLMs), API Integration, Data Management, Distributed Computing, Amazon DynamoDB, Apache Spark, Spark, Data-driven Marketing, Pricing Models, Generative Pre-trained Transformer 3 (GPT-3), OpenAI GPT-4 API, Software Architecture, Graphics Processing Unit (GPU), Convolutional Neural Networks (CNN), Image Analysis, Machine Learning Operations (MLOps), Cloud Architecture, Project Management, Models, JavaScript, Prompt Engineering, Instruction Tunning

PhD Researcher

2021 - 2022
Nantes Université
  • Worked on my PhD thesis: The Acceleration of a Neural Network Using Computer Arithmetic.
  • Devised new neural network training strategies to encourage better performances from low-precision neural networks.
  • Developed custom CUDA operations for low-level operations and function approximations.
  • Developed RL algorithm to solve optimization problems in large categorical observations spaces.
  • Attended conferences and research schools related to machine learning and computer arithmetic.
Technologies: PyTorch, Arithmetic, Neural Networks, NumPy, Jupyter Notebook, Pandas, Machine Learning, Python, Artificial Intelligence (AI), C++, Data Science, Image Processing, Deep Neural Networks, Matplotlib, AI Design, Algorithms, Internet of Things (IoT), Slurm Workload Manager, Data Analysis, Statistics, Python 3, Datasets, DeepSpeed, Causal Inference, Fine-tuning, Data Inference, Scikit-learn, Programming, Bash, NVIDIA CUDA, Docker, Image Recognition, Handwriting Recognition, Software Architecture, Graphics Processing Unit (GPU), Convolutional Neural Networks (CNN), Image Analysis, Machine Learning Operations (MLOps), Project Management, Models

Researcher

2021 - 2021
Inria
  • Worked on a research project to enable running big machine learning models on small resource-constrained devices using early exit networks.
  • Collaborated with two professors to do a literature review on model compression techniques, including quantization, pruning, and knowledge distillation.
  • Implemented a reinforcement learning solution (DQN) to solve an optimization problem.
  • Published a paper for an international conference and presented the work at the meeting.
Technologies: PyTorch, Deep Neural Networks, Microsoft Edge, NumPy, Jupyter Notebook, Pandas, Machine Learning, Computer Vision, Python, Artificial Intelligence (AI), C++, Data Science, Image Processing, Matplotlib, AI Design, Algorithms, OpenCV, Computer Vision Algorithms, Internet of Things (IoT), Slurm Workload Manager, Statistics, Python 3, Datasets, Causal Inference, Fine-tuning, Data Inference, Scikit-learn, Programming, Bash, Docker, API Integration, Graphics Processing Unit (GPU), Convolutional Neural Networks (CNN), Image Analysis, Models

Machine Learning Engineer

2020 - 2020
Navya
  • Made an object detection model faster and lighter to be able to run in pseudo real time to be deployed on a self-driving car.
  • Worked closely with the core machine learning team in order to make sure we were aligned on the experiment setup and results.
  • Experimented with multiple model compression strategies like pruning, quantization, and compiling to evaluate the efficacy of each method.
  • Compressed the model around 4x with 3x improvement in inference speed while maintaining the same performance as the original model.
Technologies: PyTorch, Computer Vision, Object Detection, Quantization, NumPy, Jupyter Notebook, Pandas, Python, Artificial Intelligence (AI), Data Science, Image Processing, Advisory, Deep Neural Networks, Matplotlib, AI Design, Data Visualization, APIs, Algorithms, Data Analytics, Predictive Modeling, OpenCV, Computer Vision Algorithms, Consulting, Predictive Analytics, Statistics, Python 3, Datasets, Causal Inference, Fine-tuning, Scikit-learn, Programming, Web Development, Analytics, Bash, Docker, MySQL, XGBoost, Object Tracking, Image Recognition, Handwriting Recognition, API Integration, Software Architecture, Graphics Processing Unit (GPU), Convolutional Neural Networks (CNN), Image Analysis, Cloud Architecture, Project Management, Models

Data Scientist

2020 - 2020
SannSyn
  • Developed a system that classifies legal cases from raw text input by an industry expert. The data given was raw scraped and OCR'd pdf from online sources.
  • Performed data analysis of unstructured data to understand what could be done with the data and the needed processes to improve the quality.
  • Built a web scraper to scrape financial news.
  • Provided a sentiment analysis feature for financial news articles using transformer-based models.
Technologies: PyTorch, Sentiment Analysis, Text Classification, NumPy, Jupyter Notebook, Pandas, Machine Learning, Computer Vision, Python, Artificial Intelligence (AI), Data Science, Image Processing, PySpark, Deep Neural Networks, Matplotlib, Data Visualization, APIs, Algorithms, Data Analytics, Predictive Modeling, OpenCV, Computer Vision Algorithms, Data Scraping, Predictive Analytics, SQL, Statistics, Python 3, Datasets, Fine-tuning, Data Inference, Scikit-learn, Programming, Amazon Web Services (AWS), NoSQL, Bash, Docker, MySQL, XGBoost, API Integration, Software Architecture, Machine Learning Operations (MLOps), Cloud Architecture, Project Management, Models

Machine Learning Engineer

2019 - 2019
Tedmob
  • Developed a customer-facing chatbot using RASA AI, Dialogflow, and Microsoft bot framework for a leading telecom operator for handling FAQ and account-related questions with OTP authentication and third-party integrations.
  • Tracked issues in real-time using various tools like Sentry, ELK stack, and Docker monitoring tools.
  • Handled meeting with the client and gathered various team requirements for the best launch process.
Technologies: PyTorch, Machine Learning, Chatbots, Recommendation Systems, TensorFlow, PySpark, NumPy, Jupyter Notebook, Pandas, Computer Vision, Python, Artificial Intelligence (AI), Data Science, Image Processing, Advisory, Deep Neural Networks, Matplotlib, Algorithms, OpenCV, Computer Vision Algorithms, Consulting, Technology Consulting, SQL, Statistics, Python 3, Datasets, Fine-tuning, Scikit-learn, Programming, Amazon Web Services (AWS), Speech Recognition, Web Development, NoSQL, Bash, Docker, API Integration, Amazon DynamoDB, Google Cloud Platform (GCP), Software Architecture, Convolutional Neural Networks (CNN), Image Analysis, Machine Learning Operations (MLOps), Cloud Architecture, Project Management, Models, JavaScript, Medical Imaging

Head of the iOS Department

2017 - 2019
Tedmob
  • Led and oversaw a team of iOS developers for two years.
  • Managed the hiring and onboarding process for new hires.
  • Migrated the team tech stack and incorporated a new software architecture.
Technologies: Swift, Jira, Xcode, Jupyter Notebook, Python, Algorithms, Technology Consulting, Programming, NoSQL, API Integration, App Development, Software Architecture, Cloud Architecture, Project Management

iOS Developer

2016 - 2017
Tedmob
  • Developed commercial applications for clients ranging from one-person startups to multinational companies.
  • Tracked issues happening on the application side in real time and resolved them in the next release.
  • Migrated old applications from Objective-C to Swift.
Technologies: Swift, iOS, Xcode, Algorithms, Mobile Development, Mobile App Development, Programming, NoSQL, API Integration, App Development, Software Architecture, Project Management

Concept Extraction from Videos

A complete set of ML models to extract concepts from video data, including transcripts, audio tracks, thumbnails, and the actual video footage.

The concepts include:

• Emotions
• Activities
• Sentiment
• Objects
• Movement

Quantized Neural Network for Object Detection

This work was done as part of a project with a self-driving car company where the aim was to take a pre-trained model and try to run it as fast as possible and as light as possible to be used for real-time object detection.

The model used is a MobileNetV2 pre-trained model on ImageNet with an SSDLite object detector. We trained the model in an FP32 data format.

I applied several model compression techniques to reduce the model's size and monitor its performance.

Some of the methods we used are:
• Quantization
• Pruning
• Fused convolution
• Knowledge distillation

The work concluded that the model could reliably detect objects in images with the same accuracy as the FP32 version while going as low as the INT8 data format.

Casual Language Model Fine-tuning

Iteratively fine-tuned a GPT-like simple language model on a large dataset to be deployed on a blockchain network. The training procedure included using DeepSpeed for model parallelism and custom loggers for monitoring using the weights and biases service.

Legal Case Classification

This project was threefolds:

• EDA on unstructured messy text data to understand what can be done with the data

• Build a model that would classify a legal case based on the description entered into several categories. This part aimed to provide a tool to help lawyers classify cases faster and more easily.

• Extract the entities from the case description relevant to the classification to help counterarguments.

Custom Language Model Training Framework

https://github.com/pegesund/nor_bert
A dataset agnostic training framework for transformer-based language models, and this project was to help bridge the performance gap between English and non-English language models. The final library should be integrated into the famous SentenceTransformer NLP package to be used directly.

Multi-modal Text Classifier

A machine learning model for extracting multiple labels from input streamed text data and I oversaw building this model end to end. The objective is to use the output of this model as a feature for another classifier.

Twitter Sentiment Analysis

A text classification model that classifies the sentiment of a single tweet or a hashtag on Twitter, where the model is a transformer-based BERT model fine-tuned on a sentiment analysis dataset. The model is hosted using Streamlit.

Running Neural Networks on Edge Devices

https://ieeexplore.ieee.org/abstract/document/9664700
I developed a novel technique for successfully executing parts of a single model through multiple devices like IoT, edge, and cloud while respecting each device's resource limitations and for that, I introduced a new offloading mechanism where, during computation, a decision can be made to offload work, together with the ability to exit early in the computation with intermediate results, where the decision itself is tuned through deep Q-learning.

Norwegian Sentiment Analysis Model

A Transformer based neural network for classifying the sentiment of financial news into three main categories:
• Positive
• Neutral
• Negative.

The main difficulty was finding a good Norwegian labeled sentiment analysis dataset and fine-tuning an existing multilingual model.
2021 - 2022

PhD Degree in Computer Science

Nantes Université - Nantes, France

2019 - 2021

Master's Degree in Artificial Intelligence

ESIEE Paris - Paris, France

2014 - 2018

Bachelor's Degree in Computer Science

Lebanese University - Beirut, Lebanon

MARCH 2020 - PRESENT

Deep Learning Specialization

DeepLearning.ai | via Coursera

MAY 2019 - PRESENT

Advanced Machine Learning Specialization

DeepLearning.AI and Stanford Online | via Coursera

MAY 2018 - PRESENT

Machine Learning

Stanford University | via Coursera

JULY 2016 - PRESENT

iOS Developer Nanodegree

Udacity

Libraries/APIs

PyTorch, NumPy, Pandas, Matplotlib, XGBoost, PySpark, TensorFlow, OpenCV, DeepSpeed, REST APIs, Scikit-learn, YouTube API, cuBLAS

Tools

Apache Airflow, Amazon EKS, Google Bard, Xcode, Jira, Jupyter, AWS ELB, Amazon Elastic Container Service (Amazon ECS), AWS Fargate, ChatGPT, Amazon Textract, Amazon SageMaker, Apache Iceberg

Languages

Python, Python 3, Bash, SQL, Snowflake, C++, Swift, JavaScript

Platforms

Jupyter Notebook, Docker, Databricks, Amazon Web Services (AWS), Embedded Linux, iOS, AWS Lambda, NVIDIA CUDA, Blockchain, Microsoft Edge, Google Cloud Platform (GCP), Kohya, MosaicML, Together.ai

Paradigms

Data Science, Mobile Development, ETL, App Development, Distributed Computing, Real-time Systems

Storage

NoSQL, MySQL, Amazon DynamoDB, Data Lakes, Amazon S3 (AWS S3)

Frameworks

Apache Spark, Spark, Flask, gRPC

Industry Expertise

Project Management

Other

Machine Learning, Computer Vision, Sentiment Analysis, Deep Learning, Natural Language Processing (NLP), Artificial Intelligence (AI), Image Processing, OCR, Computer Vision Algorithms, Mobile App Development, Predictive Modeling, Algorithms, APIs, Data Analytics, Data Visualization, Forecasting, AI Design, Data Engineering, Deep Neural Networks, Data Analysis, CTO, Predictive Analytics, Statistics, Datasets, Causal Inference, Fine-tuning, Data Inference, Programming, Architecture, Analytics, Image Recognition, API Integration, Generative Pre-trained Transformers (GPT), Software Architecture, Graphics Processing Unit (GPU), Convolutional Neural Networks (CNN), Image Analysis, GPU Computing, Cloud Architecture, Models, Supervised Learning, Instruction Tunning, Optimization, Consulting, Advisory, Technology Consulting, Startup Consulting, Facial Recognition, OpenAI, GPT Neo, Google Publisher Tag (GPT), Text Generation, Speech Recognition, Web Development, Handwriting Recognition, Large Language Models (LLMs), Data Management, Stable Diffusion, ControlNet, Machine Learning Operations (MLOps), Data-driven Marketing, Generative Pre-trained Transformer 3 (GPT-3), OpenAI GPT-4 API, Research, Outlier Detection, Unsupervised Learning, Prompt Engineering, Retrieval-augmented Generation (RAG), Medical Imaging, Graph Theory, Arithmetic, Object Detection, Neural Networks, Slurm Workload Manager, Chatbots, Recommendation Systems, Quantization, Data Scraping, Clustering, Text Classification, Internet of Things (IoT), Language Models, Data Warehousing, Trend Forecasting, Topic Modeling, Revenue Projections, Video Analysis, Speech Synthesis, Sound, Text Animation, Diffusion Models, Image Generation, Quantitative Finance, Object Tracking, Generative Models, Plugins, Generative Pre-trained Transformer 4 (GPT-4), OpenAI GPT-3 API, YouTube Ads, Videos, LoRa, Flash Attention, Pricing Models, DreamBooth, Wearables, Biometrics, Reinforcement Learning, Text to Image, Text to Video, MLflow, Midjourney, Avatars, DQN, Modeling, LLaMA, Deep Reinforcement Learning, AI Programming, Game Development, Mobile Games, Data, Generative Artificial Intelligence (GenAI), Generative AI, Llama 2, Lottie, AI-generated Video, Distributed Cloud, AI Model Training

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