Europe Research Interest Group Meeting [February 2023]
Date: February 9, 2023
Synergizing MLOPS, DataOps, Data Quality, and Software Engineering: A Research-Driven Approach to ML System Development, Deployment and Maintenance by Bestoun S. Ahmed Al-Beywanee, Karlstad University
This talk will provide a research-focused perspective on MLOPS, DataOps, Data Quality, and Software Engineering synergy. Attendees will gain insights into the latest developments and challenges in these fields from a research perspective and learn about the current state-of-the-art in these areas. The talk will explore how these fields can be integrated to improve the development, deployment, and maintenance of machine learning systems in industry and academia. The talk will highlight the importance of collaboration between data scientists and software engineers and how to establish an MLOPS pipeline from the software engineering and quality assurance perspective. The talk will interest researchers, data scientists, engineers, and anyone interested in learning about the latest research and advancements in these areas and how they can be applied to improve their workflows and outcomes. Several case studies and solutions will be presented from industry-academia collaborations projects.
Bestoun S. Ahmed, Ph.D., is an associate professor at Karlstad University and an excellent researcher at the Czech Technical University in Prague. He spent several years researching abroad in many international institutes and collaborating with many industrial partners. He has a strong background in software engineering, artificial intelligence, machine learning, and AI Engineering. He has published several papers in leading journals and conferences in his field and has significantly contributed to developing new algorithms and techniques for solving complex problems. His current research focuses on software engineering for AI solutions and quality assurance in data-driven systems. He is also interested in applying MLOPs on the industrial scale and ensuring high data quality using software engineering concepts.
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