Source: https://unsplash.com/@kellysikkema

Carrying an AI model into production is, of course, not the same as getting traditional software into production (you’ve probably read what Google has to say about it but just in case…). In ML pipelines, the entire infrastructure is different: there are many teams involved in the process, the data is central, performance is measured with ML-specific KPI’s, and debugging does not involve ‘if/else’ code, etc.

The result of the unique nature of AI and the challenges it presents causes Data Science teams to believe they should rethink production readiness — if not reinvent the readiness wheel from scratch. …

Zohar Einy

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