DenizBank drives AI innovation with Red Hat OpenShift AI

DenizBank drives AI innovation with Red Hat OpenShift AI The challenge: Overcoming barriers to productivity and innovation The approach: Building a foundation for AI at scale OpenShift AI in action: What was accomplished Measurable success: The compelling results Pioneering the future of banking Get started with AI for enterprise: A beginner’s guide About the authors Erkan Ercan Will McGrath More like this Blog post Blog post Blog post Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share In the competitive world of modern finance, staying ahead means embracing innovation. For DenizBank, one of Türkiye's leading private banks, this means a strong commitment to integrating artificial intelligence (AI) and machine learning (ML) into the core of its operations. Driving this technological evolution within DenizBank is Intertech, DenizBank's IT subsidiary, which has played a key role in redefining what's possible in banking. Before this modernization, DenizBank's data science teams faced significant hurdles that hindered their ability to innovate quickly. Their workflows were characterized by: Manual and inflexible environments : Over 120 data scientists were using a Virtual Desktop Infrastructure (VDI) for model development. This VDI-based environment was not only slow and resource-intensive, but it also made lifecycle management of continuously changing Python libraries incredibly difficult. It forced them to rely on cumbersome digital workstations where each new model required a complex, manual setup. A lack of standardization : Without a unified platform, consistency was a major issue. Data access methods, model environments, database integrations, and code repositories varied from one data scientist to another, making collaboration and management difficult. Slow time-to-market and deployment : The manual processes created a development bottleneck. It was also taking a significant amount of time to decide which model to deploy, as business teams struggled to understand and compare which models performed better. This indecision meant that critical business opportunities were being missed.