This is the third post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. This post is intended for data scientists, MLOps engineers, and data engineers who are interested in building ML pipeline templates with Amazon SageMaker. We explain how NatWest Group used SageMaker to create standardized end-to-end MLOps processes. This solution reduced the time-to-value for ML solutions from 12 months to less than 3 months, and reduced costs while maintaining NatWest Group’s high security and auditability requirements.
NatWest Group chose to collaborate with AWS Professional Services given their expertise in building secure and scalable technology platforms. The joint team worked to produce a sustainable long-term solution that supports NatWest’s purpose of helping families, people, and businesses thrive by offering the best financial products and services. We aim to do this while

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