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 multi-plane 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.