Founding Engineer
2022 - PRESENTIntramove.ai- Engineered core microservices to process payments, register users, keep track of API credits, and perform text analysis.
- Created a PyPI API client for developers to interact with the server.
- Built a website to demonstrate Intramove.ai, an API product for developers interested in analyzing economic news. The core service is powered by SOTA deep learning models for natural language and has endpoints to analyze headlines and articles.
Technologies: Amazon EC2, Docker, PyTorch, PyPI, Stripe API, MongoDB, FastAPI, Python, GitHub, Amazon Route 53Senior Machine Learning Engineer
2022 - PRESENTRepustate- Developed interpretable, supervised deep learning solutions for text using PyTorch and for implementing and improving research paper algorithms, such as BERT, custom attention layers, and hierarchical attention networks.
- Created interpretable, unsupervised deep learning algorithms that eliminated Repustate's need for tagged data when building custom models, enabling scalable growth and taking the company's core service to the next level.
- Designed and developed a new generation gRPC microservices API, allowing the main application developed in Go to communicate with Python deep learning servers.
- Increased inference speed by 2-3x on average for prediction tasks using different techniques, such as ONNX quantization or serving and chunks batching.
- Reduced the turnaround time for onboarding clients with custom needs from an average of 4 weeks to 1-3 days using the new generation API and ML pipeline.
- Built an in-house multilingual transcription service that replaced Amazon Transcribe, reducing annual expenses by 14x.
- Managed a multi-client portfolio, led technical discussions on sales calls, and aided in landing 10+ clients using the new generation API.
- Devised and developed Amazon S3 schemas for production models and tokenizers.
- Delivered custom deep-learning Docker images and RPM packages to be installed in on-premise servers.
- Architected and developed a custom MLflow tracking server to record and monitor experimentation results and artifacts.
Technologies: Artificial Intelligence (AI), Amazon S3 (AWS S3), Algorithms, Deep Learning, Open Neural Network Exchange (ONNX), Hugging Face, gRPC, Protobuf, Git, Go, Python, PyTorch, Keras, MLflow, Amazon EC2, Docker, NumPy, Scikit-learn, PandasData Scientist
2020 - PRESENTDecathlon- Developed an in-house data-visualization pipeline that replaced a licensed tool saving $60,000 per year. Used SQL, Git, Jenkins, AWS cloud, Google Sheets API, and Google Data Studio.
- Prototyped an NLU solution with customer reviews classification, keyword extraction, and sentiment analysis that outperformed a licensed tool, saving the marketing team $15,000 per year.
- Created a visual search engine that was deployed as a product retrieval API. It's currently being used for product recommendations.
- Built an unsupervised topic modeling solution for customer reviews with visualization, using sentence transformers. Improved original solution using GPT2 and prompt engineering.
- Developed a store turnover forecasting tool using additive models and custom-made regressors (Prophet API).
- Engineered an NLU product-article recommendation solution as part of Decathlon's personalization strategy.
- Worked on data extraction, transformation, and loading tasks for each solution.
- Made a sustainability reporting tool to monitor the performance of second-life and eco-designed products.
- Built a color detection solution using k-means clustering to aid internal object detection models.
- Interviewed new data science candidates, actively contributed to the hiring process, and mentored new interns on various data-related tasks.
Technologies: Python, SQL, Jenkins, Keras, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, Flask, Google Data Studio, Redshift, Amazon S3 (AWS S3), Google Cloud Platform (GCP), Jenkins Pipeline, GitHubMachine Learning Intern
2019 - 2019Decathlon- Developed and deployed a deep recommendation model for user-click item prediction using LSTM RNN architecture.
- Surpassed the benchmarked precision, recall, and coverage metrics by improving the solution using attention models.
- Developed and deployed an object detection model using TensorFlow's API for a hockey-brand detection application.
Technologies: Python, Algorithms, Keras, SQL, TensorFlow, Jenkins Pipeline, GitHubMachine Learning Developer Intern
2018 - 2018Societe Generale- Developed a BI reporting tool using MicroStrategy.
- Contributed to data visualization projects using Tableau.
- Helped develop a web application using the Django framework.
Technologies: Tableau, Python, Django, MicroStrategy
Abdellatif Dalab
Data Scientist and Software Developer in Montreal, QC, Canada
Member since December 8, 2021
Abdellatif is a machine learning engineer and data scientist passionate about mathematics and applied research. He currently leads the ML engineering initiative at Repustate, where he develops and deploys custom, multilingual deep learning models for various natural language applications. Abdellatif also designs and engineers deep learning API servers to support the company's core natural language services.
Abdellatif is now available for hire
Portfolio
- Intramove.aiAmazon EC2, Docker, PyTorch, PyPI, Stripe API, MongoDB, FastAPI, Python...
- RepustateArtificial Intelligence (AI), Amazon S3 (AWS S3), Algorithms, Deep Learning...
- DecathlonPython, SQL, Jenkins, Keras, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy...
Availability
Part-time
Preferred Environment
Google Colaboratory (Colab), Visual Studio Code, Jupyter Notebook, MacOS, GitHub
The most amazing...
...project I've worked on at Repustate is designing and developing a new generation v5 DL microservices API that communicates with a monolith Go-based application.
Employment
Experience
- AI Game Project and Reporthttps://github.com/abdelatifsd/Comp-472---MiniMax-Algorithm
This project was part of the AI course (COMP 472) at Concordia University. It includes a detailed report of the implementation strategy, minimax algorithm, and outcome analysis. The Jupyter Notebook has most of the code. The project didn't utilize any existing libraries. All algorithms and heuristics were implemented from scratch.
Skills
Languages
Python, SQL, HTML, CSS, JavaScript, GoLibraries/APIs
Keras, Scikit-learn, NumPy, Pandas, PyTorch, TensorFlow, React, Protobuf, Stripe APIOther
Artificial Intelligence (AI), Data Analysis, Machine Learning, Natural Language Processing (NLP), Google Data Studio, Deep Learning, OOP Designs, Data Structures, Algorithms, Principles of Programming Languages, Hugging Face, MicroStrategy, Google Colaboratory (Colab), Open Neural Network Exchange (ONNX), MLflow, FastAPI, Amazon Route 53Tools
Jenkins, GitHub, Tableau, Git, PyPIStorage
Redshift, PostgreSQL, Amazon S3 (AWS S3), MySQL, Google Cloud Datastore, Database Programming, MongoDBFrameworks
Flask, Django, gRPCParadigms
Database DesignPlatforms
Google Cloud Platform (GCP), Visual Studio Code, Jupyter Notebook, Docker, MacOS, Amazon EC2
Education
- Bachelor's Degree in Information Technology2015 - 2019Concordia University - Montreal, Quebec, Canada
Certifications
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep LearningSEPTEMBER 2020 - PRESENTCoursera
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and OptimizationJANUARY 2018 - PRESENTCoursera
- Structuring Machine Learning ProjectsJANUARY 2018 - PRESENTCoursera
- Neural Networks and Deep LearningAUGUST 2017 - PRESENTCoursera
- Machine Learning with Python in Data ScienceJULY 2017 - PRESENTUdemy
- Machine LearningAPRIL 2017 - PRESENTStanford University | via Coursera
