Evangelos Nerantzis was born on June 23, 1978, in Xanthi, Greece. He completed his undergraduate studies at the Department of Forestry and Management of the Natural Environment, School of Agricultural Technology, Food Technology and Nutrition, of the Technological Educational Institute (TEI) of Kavala, from which he graduated in 2003 (Grade: 6.80/10). His undergraduate thesis, entitled “Development of Educational Material in Digital Form: Avifauna,” was awarded a grade of 10/10. 

He pursued postgraduate studies at the Democritus University of Thrace, in the Master’s Degree Programme “Advanced Informatics and Computing Technologies,” from which he received his Master of Science degree with a grade of 9.29/10 (Excellent) in 2025. His Master’s thesis, entitled “MLOps for Artificial Vision Applications with Multi-Model Deep Learning Data Pipelines,” was awarded a grade of 10/10. He is currently a PhD candidate in the field of Artificial Intelligence at the Democritus University of Thrace. 

From 2005 to 2015, he worked as a self-employed professional in the geotechnical sector, undertaking development plans and studies. He subsequently worked on development and investment programmes (2015–2019), while during the period 2018–2020 he served as a member of the scientific committee of the Geotechnical Chamber of Greece on Pharmaceutical and Industrial Cannabis. 

Since 2020, he has been collaborating with the Athena Research and Innovation Center as an affiliated researcher, participating in national and co-funded projects. These include the projects Smart-BioreAL (2020–2023), AGRO4+ (2021–2023), and ALGEBRA (2024–2025), as well as activities supporting the Center’s General Directorate. His research work within the above projects focuses on the development of smart photobioreactors, the cultivation of microalgae (Chlorella vulgaris, Arthrospira platensis), wastewater bioremediation, and the design and implementation of machine learning pipelines for image analysis and industrial applications. 

His research interests focus on Machine and Deep Learning, MLOps practices (CI/CD/CT), Computer Vision, Explainable AI models, the energy profiling of computational models, as well as the development of multi-agent systems and intelligent IoT applications. He has extensive experience in the design and implementation of convolutional neural networks (CNNs) and U-Net architectures, as well as in the adaptation of lightweight architectures through the fine-tuning of fully connected layers. 

He has published papers in international scientific journals and conference proceedings (IEEE, ACM, Springer, MDPI), on topics relating to MLOps, CNN architectures, computer vision pipelines, model energy profiling, and machine learning applications in industrial and agri-food systems. His published work also includes datasets for computer vision applications.