Continuous delivery for machine learning

www.thoughtworks.com

As organizations move to become more “data-driven” or “AI-driven”, it’s increasingly important to incorporate data science and data engineering approaches into the software development process to avoid silos that hinder efficient collaboration and alignment. However, this integration also brings new challenges when compared to traditional software development.

[...] machine learning applications are often developed in isolation and never leave the proof of concept phase. If they make it into production, this is often a one-time ad-hoc process that makes it difficult to update and re-train them, leading to stale and outdated models. machine learning applications are often developed in isolation and never leave the proof of concept phase. If they make it into production, this is often a one-time ad-hoc process that makes it difficult to update and re-train them, leading to stale and outdated models. 

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