{"slug": "building-ailef-a-universal-engineering-framework-for-the-ai-lifecycle", "title": "Building AILEF: A Universal Engineering Framework for the AI Lifecycle", "summary": "A developer at the École Supérieure Polytechnique of Dakar (ESP-UCAD) and UMMISCO has proposed SDLC-AI, a universal engineering framework for AI systems inspired by the Software Development Life Cycle. To operationalize it, they built AILEF (AI Lifecycle Engineering Framework), a platform that automates the entire AI lifecycle, addressing challenges in reproducibility, traceability, governance, deployment, monitoring, and continuous retraining.", "body_md": "Alhamdulillah!\n\nI am pleased to share that I have successfully defended my Master's thesis in Artificial Intelligence and Big Data at the École Supérieure Polytechnique of Dakar (ESP-UCAD).\n\nThis research was conducted during my internship at UMMISCO (International Joint Unit for Mathematical and Computer Modeling of Complex Systems).\n\nThe Problem\n\nToday, building machine learning models has become easier than ever.\n\nBuilding reliable AI systems is a different challenge.\n\nWhile software engineering has relied for decades on standardized development processes such as the Software Development Life Cycle (SDLC), AI projects are still often developed using ad hoc and empirical practices.\n\nAs a result, many AI projects struggle with:\n\n• Reproducibility\n\n• Traceability\n\n• Governance\n\n• Deployment\n\n• Monitoring\n\n• Model maintenance\n\n• Continuous retraining\n\nOur Contribution\n\nTo address these challenges, we proposed SDLC-AI, a universal engineering framework inspired by the traditional Software Development Life Cycle and adapted to the specific requirements of AI systems.\n\nTo operationalize this framework, we designed and implemented AILEF (AI Lifecycle Engineering Framework), a platform that supports and automates the entire AI lifecycle.\n\nThe platform automates several lifecycle engineering tasks, allowing data scientists to focus on developing and improving machine learning models while the platform handles the underlying engineering complexity.\n\nWhat I Learned\n\nThis project was much more than a Master's thesis. It gave me the opportunity to deepen my knowledge in:\n\n• Artificial Intelligence\n\n• Machine Learning\n\n• Deep Learning\n\n• Software Engineering\n\n• MLOps\n\n• ModelOps\n\n• DevOps\n\n• Explainable AI (XAI)\n\n• AI Governance\n\n• Model Monitoring\n\n• Continuous Retraining\n\n• Scientific research\n\nAcknowledgements\n\nI would like to express my sincere gratitude to Mr. Mandicou Ba, Dr. Fatou Ngom, Prof. Alassane Bah, Mr. Mouhamed Amar, the researchers at UMMISCO, the members of the examination committee, the faculty of ESP-UCAD, my family, my friends, and everyone who supported me throughout this journey.\n\nThis achievement marks the end of one chapter and the beginning of another. I look forward to continuing my research and contributing to the development of more reliable, responsible, and industrialized AI systems.\n\nThank you for reading.", "url": "https://wpnews.pro/news/building-ailef-a-universal-engineering-framework-for-the-ai-lifecycle", "canonical_source": "https://dev.to/ibrahima_faye_e3e45264f52/building-ailef-a-universal-engineering-framework-for-the-ai-lifecycle-2do2", "published_at": "2026-07-17 17:50:31+00:00", "updated_at": "2026-07-17 18:30:00.704752+00:00", "lang": "en", "topics": ["machine-learning", "mlops", "ai-infrastructure", "ai-research", "developer-tools"], "entities": ["École Supérieure Polytechnique of Dakar", "ESP-UCAD", "UMMISCO", "SDLC-AI", "AILEF"], "alternates": {"html": "https://wpnews.pro/news/building-ailef-a-universal-engineering-framework-for-the-ai-lifecycle", "markdown": "https://wpnews.pro/news/building-ailef-a-universal-engineering-framework-for-the-ai-lifecycle.md", "text": "https://wpnews.pro/news/building-ailef-a-universal-engineering-framework-for-the-ai-lifecycle.txt", "jsonld": "https://wpnews.pro/news/building-ailef-a-universal-engineering-framework-for-the-ai-lifecycle.jsonld"}}