Today, with the rapid advancements in technology, research, and tools, building machine learning models has become increasingly accessible. Delivering them consistently, reliably, and at scale in a real business environment? That’s where the real challenge begins.

In this talk, I’ll share how we designed and deployed an enterprise-grade, fully automated ML pipeline at Bankaya, built to enable continuous integration, delivery, and automated retraining of machine learning models across multiple business areas like risk, collections, and marketing.

This infrastructure empowers our data science team to retrain and redeploy models with minimal manual effort, significantly reducing the time from experimentation to production. But beyond speed, the real value lies in scalability and reusability: the same pipeline powers dozens of models across the company, all with standardized monitoring, governance, and traceability.

One of the most impactful results of this automation is that our data scientists can now focus on what truly drives model performance: understanding the dynamics behind each use case, designing better features, and improving model explainability. By reducing the manual workload in between, we’ve enabled the team to concentrate on higher-value tasks that enhance both accuracy and transparency.

By building this unified system, we transformed machine learning from isolated experiments into an enterprise capability, turning models into products ready to scale with the business.