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Défense de mémoire de Monsieur Benjamin Jonard

Automating ML Pipelines: A MLOps-GitOps Workflow implementation

Catégorie : mémoire
Date : 17/06/2025 09:00 - 17/06/2025 10:30
Lieu : Salle académique
Orateur(s) : Benjamin Jonard
Organisateur(s) : Benjamine Lurquin

 

 

Machine learning projects face significant deployment challenges, with only a small percentage

successfully reaching production due to operational complexity.

This thesis addresses these

challenges by developing and implementing a state-of-the-art MLOps-GitOps work#ow designed

to support machine learning projects from initiation through production maturity.

Following a comprehensive literature review of current MLOps methodologies and frame-

works, we implement a modular work#ow using a two-phase approach: initial validation with a

controlled demonstration model, followed by a design tailored for integration with the real-world

LSFB (Langue des Signes de Belgique Francophone) production system.

The implementation integrates enterprise-level technologies including Kubernetes, Docker,

Helm, Airflow, and Kubeflow pipelines, applying GitOps methodology consistently across DataOps

and MLOps processes. The resulting work#ow successfully demonstrates how a state-of-the-art

MLOps-GitOps approach can bootstrap machine learning projects and support their progres-

sion toward greater maturity, providing a reusable foundation for diverse data engineering and

Keywords:

machine learning projects across various organizational scales.

MLOps, DevOps, DataOps, GitOps, Machine Learning Deployment, CI/CD, Pipeline

Automation, Kubeflow, Airflow, Python, Kubernetes

 

Contact : Benjamine Lurquin - 081725255 - secretariat.info@unamur.be
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