Case Studies, DevOps, DevSec

Developing a modern machine learning infrastructure – Mastercard Case Study

enterprise:CODE is the enterprise software development event designed for team leads, architects, and project management and is organized for developers, by developers from the industry.

Due to the COVID-19 lockdown, the event took place with a digital format last April 21- 22. The digital sessions ran over 2 days, giving the audience the chance to learn, engage and discuss DevOps and best practices and tooling strategies in real-time with thought leaders across the globe – directly from their desk.

The live version of enterprise:CODE 2020 will take place next November 30 – December 01, 2020 in Berlin, Germany. Click here for more information.

In the online session entitled ‘Developing a modern machine learning infrastructure’ Steve Flinter, Artificial Intelligence Practice Lead at Mastercard outlined the work that Mastercard Labs undertook to build an end-to-end machine learning pipeline, suitable for both R&D and production, using Kubernetes and Kubeflow.

He demonstrates how the pipeline can be defined in software, configured, connected to a data streaming service (Apache Kafka), and used to train and deploy a model, which can be exposed for inference via an API.

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Take part in enterprise:CODE: Nov 30 – Dec 01, 2020 │ Berlin, Germany