Scaling Machine Learning with Data Mesh


With the quick rise in popularity of Data Mesh we now approach new frontiers in the Data Mesh space to solve for more complex scenarios such as model training at scale. This talk will discuss how to architect your Data Mesh platform to create scalable self service Machine Learning Data Products. Thereby allowing both Data Scientists and Machine Learning Engineers to easily provision and deploy infrastructure reducing time to market while also gaining all the benefits of Data Mesh.

I will focus on the common use case of anomaly detection in a closed-loop Convolutional Neural Network (CNN) to demonstrate the benefits of adopting the Data Mesh paradigm across a multiplane data platform in Machine Learning operations. With this example we will learn how to make the leap from model experimentation to productisation while adhering to the common affordances of a data product such as observability, life-cycle management and discoverability.


Shawn is passionate about harnessing the power of data strategy, engineering and analytics in order to help businesses uncover new opportunities. As an innovative technologist with over 13 years experience, Shawn removes technology as a barrier, and broadens the art of the possible for business and product leaders. His holistic view of technology and emphasis on developing and motivating strong engineering talent, with a focus on delivering outcomes whilst minimising outputs, is one of the characteristics which sets him apart from the crowd.

Shawn’s deep technical knowledge includes distributed computing, cloud architecture, data science, machine learning and engineering analytics platforms. He has years of experience working as a consultant practitioner for a variety of prestigious clients ranging from secret clearance level government organizations to Fortune 500 companies.

Open Data Science




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