Data/Analytics
MLOps: How SR-Bank manages AI lifecycles
Oliver Hebnes
Stavangerloftet
MLOps, or Machine Learning Operations, is a practice that bridges the gap between data science and operations, aiming to streamline and automate the lifecycle of machine learning models. It facilitates rapid deployment, monitoring, and lifecycle management of ML models that predict market trends, detect fraudulent transactions, and customize client services. ML-Ops is becoming increasingly ubiquitous in the modern era of banking. This presentation briefly demonstrates how SR-Bank uses ML-Ops to improve efficiency, security, and time-to-market.
Oliver Hebnes is a seasoned MLOps Engineer in SR-Bank, where he specializes in the integration of machine learning methodologies with robust coding practices to enhance financial operations. A physicist by training, Oliver completed his MSc in computational physics at the University of Oslo (UiO), where he studied new quantum material platforms using machine learning methods. Transitioning from computational physics to the practical realms of technology, Oliver's professional journey has spanned multiple industries, including energy and educational technology.
At SR-Bank, Oliver leverages this expertise to oversee the machine learning lifecycle, ensuring models are not only accurate and efficient but also maintainable and scalable. His work is key in ensuring the bank's reputation for innovation and security.
Oliver Hebnes
