This is the second of two articles. Read also:
In Part One of this series, we made the case for using chaos engineering to enhance the reliability of machine learning (ML) pipelines. The heart of any successful ML operation is its infrastructure — the data pipelines, model registries and feature stores. These are the components most susceptible to the kinds of failures that chaos engineering is designed to expose.
Once you’re aware of the most common failure modes in ML pipelines, the next question is: How do you safely simulate these…
