Machine Learning Engineering Projects in Paris
Machine Learning Engineering Projects in Paris
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Júlio Silva
Customer Churn Prediction Pipeline
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31
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Ismail ait-lahssen
Production-oriented credit risk ML system with reproducible training, saved artifacts, automated tests, and CLI-based inference workflows.
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23
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Sathwika Bandaru
Collaborative project, contributed key production components of Med-V²QA, a clinical Visual Question Answering system built on the MUMC architecture for radiology image analysis. My contributions: built the Dual-Gate Guardrail System (Gate 1 rejects non-medical intent, Gate 2 uses a local CLIP classifier to reject non-medical images ensuring clinical safety and system focus); developed the Batch Triage module using zero-shot CLIP to automatically sort and prioritise the most abnormal scans from uploads of up to 20 images; integrated Voice I/O using OpenAI Whisper for speech-to-text transcription and gTTS for audio readout of answers; built the PDF report generation module producing structured clinical reports with patient data, notes, and VQA results; and developed the FastAPI backend handling high-performance async request processing across all services. Built as part of a team my work covered the safety layer, multimodal I/O, clinical reporting, and backend API infrastructure.
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28
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Yanis Emeriau
AI Sales Predictive Modeling for General Electric
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4
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Ismail ait-lahssen
Production-oriented credit risk ML system with reproducible training, saved artifacts, automated tests, and CLI-based inference workflows.
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29
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Sathwika Bandaru
Built as part of a team, my contributions covered the pipeline orchestration, model serving, and data infrastructure layers.
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36
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Yanis Emeriau
AI Predictive Modeling for Cancer - Gustave Roussy
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4
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Ismail ait-lahssen
Developed a production-oriented ML repository with modular source architecture, automated testing, CI workflows, inference APIs, and structured engineering documentation following real-world software development practices.
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34
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Sathwika Bandaru
Built a Medical NLP system that processes clinical text to extract healthcare entities, redact sensitive information, and structure unstructured medical data using NER pipelines and FastAPI for backend integration.
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44
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Ismail ait-lahssen
Built a modular command-line interface orchestrating training, evaluation, artifact persistence, and prediction workflows through reusable pipeline components and configurable runtime parameters.
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39
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Ismail ait-lahssen
Implemented automated testing across APIs, preprocessing, inference, evaluation, persistence, CLI workflows, and end-to-end pipeline execution to improve reliability and maintainability of the ML system.
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41
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Ismail ait-lahssen
Designed a modular ML architecture with dedicated layers for APIs, feature engineering, inference, persistence, testing, and artifact management to support reproducible training and scalable prediction workflows.
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41
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Ismail ait-lahssen
Production-oriented ML system built with a modular architecture separating data processing, feature engineering, inference, API services, persistence, and training pipelines for scalable and maintainable deployment workflows.
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47
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