BBVA collaborated with AWS to implement the MLOps (Machine Learning Operations) framework on ADA, a global analytics, data, and AI platform, to enable more scalable and controlled management of the lifecycle of AI models. This architecture automates operational tasks and validation processes and unifies development, testing, and deployment.Solutions for banking technology operations. The companies announced the solution at the annual AWS Summit event.
This architecture facilitates the work of ADA’s more than 6,500 users, including the 1,000 data scientists who develop AI-based solutions at BBVA, and enables rapid creation and deployment across the group. Pilot projects include personalized recommendations to clients and financial forecasting Reduced development time by 20-75%infrastructure operating costs were optimized by 40-55 percent.
The MLOps architecture also includes governance of the AI model development cycle. of The system automates Validation, traceability, and control processes allow models to move safely into production while keeping bank review and approval mechanisms intact. Additionally, we maintain a central audit trail to ensure that all machine learning models generated at BBVA comply with security and transparency standards applicable to the financial sector. This system is essential in areas where traceability, security and risk management are important.
“Artificial intelligence creates real value only when it can be scaled industrially across the organization. The new MLOps architecture gives us a competitive advantage.” To accelerate the transformation of internal operations Natalia Sampietro from BBVA’s Data & Analytics Enablement team said:
This transformation is based on Amazon SageMaker AI, AWS’s ecosystem of tools for building, training, deploying, and managing machine learning and artificial intelligence models. One of the main advances in this solution is that Creating a temporary development environment Enables multiple teams to work on AWS cloud-based machine learning infrastructure. experiment and verify New features can be executed in parallel without interfering with each other or impacting the shared environment. Once testing is complete, resources are automatically removed, accelerating development cycles and optimizing infrastructure usage.
“At AWS, we are very proud to collaborate with BBVA on this transformation that will enable more than 6,500 data professionals to accelerate the creation and deployment of AI models with autonomy and rigor. With this MLOps architecture, BBVA is demonstrating its innovative vision and commitment to scaling AI.” Secure and agile globally,” said Carlos Alegre VergesGlobal Head of AWS and BBVA relationship.
The case study was presented at the AWS Madrid Summit, the company’s annual business event, where AWS shared its report “Unlocking the Potential of AI in Spain” with more than 10,000 attendees. The report also highlights a joint project with BBVA as an example of technological transformation and advanced adoption of artificial intelligence in corporate environments.
