BBVA and AWS Advance Enterprise AI Adoption with Governed MLOps Architecture for Banking Operations

BBVA and AWS Advance Enterprise AI Adoption with Governed MLOps Architecture for Banking Operations

(IN BRIEF) BBVA has developed a new MLOps architecture with Amazon Web Services to accelerate the development, validation and deployment of artificial intelligence models across the Group. Integrated into ADA, BBVA’s global cloud-based data and AI platform, the solution enables more than 6,500 users, including around 1,000 data scientists, to work with greater autonomy, reuse shared components and automate governance controls. In pilot use cases such as personalised customer recommendations and financial forecasting, the architecture has reduced development times by 20% to 75% and lowered infrastructure operating costs by 40% to 55%. Built on Amazon SageMaker AI, the system includes automated validation, traceability and control processes, as well as centralised audit trails to support security, transparency and regulatory compliance in banking. The project was presented at the AWS Madrid Summit and highlighted in AWS’s Unlocking the Potential of AI in Spain report as an example of advanced AI adoption in enterprise environments.

(PRESS RELEASE) BILBAO, 5-Jun-2026 — /EuropaWire/ — BBVA has developed a new technology architecture in collaboration with Amazon Web Services to accelerate the development, validation and deployment of artificial intelligence models across the Group. The new MLOps architecture has been integrated into ADA, BBVA’s global cloud-based data and artificial intelligence platform, enabling teams to build AI solutions more efficiently, reuse common components and automate governance controls.

The architecture is designed to support the full lifecycle of machine learning models, from development and testing to validation, deployment and monitoring. By embedding the framework into ADA, BBVA is strengthening its ability to scale AI-based solutions across its global operations while maintaining the governance, traceability and risk controls required in the financial sector.

The solution supports more than 6,500 users of ADA, including around 1,000 data scientists working on AI-driven solutions within BBVA. By providing a more standardised and automated operating model, the architecture gives teams greater autonomy and helps them move more quickly from experimentation to production.

In pilot projects such as personalised customer recommendations and financial forecasting, BBVA has reduced development times by between 20% and 75%. The architecture has also helped optimise infrastructure operating costs by 40% to 55%, demonstrating its potential to improve both speed and efficiency in the development of AI applications.

A key feature of the MLOps architecture is the integration of governance into the AI development cycle. The system automates validation, traceability and control processes, allowing models to move safely into production while preserving BBVA’s existing review and approval mechanisms. It also maintains a centralised audit trail to ensure that machine learning models developed at BBVA comply with the security, transparency and regulatory standards expected in banking.

Natalia Sampietro, from BBVA’s Data & Analytics Enablement team, said artificial intelligence creates real value when it can be scaled industrially across an organisation. She noted that the new MLOps architecture gives BBVA a competitive advantage by accelerating the transformation of internal operations and enabling the bank to deliver secure and transparent AI solutions to customers more quickly.

The architecture is based on Amazon SageMaker AI, AWS’s set of tools for building, training, deploying and managing machine learning and artificial intelligence models. One of the main advances is the creation of ephemeral development environments on AWS cloud-based machine learning infrastructure. These environments allow multiple teams to work, experiment and validate new functionalities in parallel without affecting shared systems or interfering with one another.

Once testing is complete, the temporary resources are automatically removed, helping accelerate development cycles while improving infrastructure efficiency. This approach supports a more agile, controlled and cost-effective way of developing AI models at scale.

Carlos Alegre Berges, Head of Sales for the financial services sector at AWS Spain, said AWS is proud to collaborate with BBVA on a transformation that enables more than 6,500 data professionals to accelerate the creation and deployment of AI models with autonomy and rigour. He said the project shows BBVA’s innovative approach and commitment to scaling AI securely and efficiently at global level.

BBVA and AWS presented the solution at the AWS Madrid Summit, the company’s annual business event. During the summit, AWS also shared its report Unlocking the Potential of AI in Spain with more than 10,000 attendees. The report highlights the BBVA project as an example of technological transformation and advanced enterprise adoption of artificial intelligence.

Media Contact:

Tel.: 944 875 555

SOURCE: BBVA