What really separates training a model from running it in production? And why does this difference matter so much at scale?

In this second part of Opening Voices, Quentin Adam, CEO of Clever Cloud, continues the discussion with Steeve Morin, founder and CEO of ZML, to clarify a distinction that is often misunderstood.

Starting from concrete explanations, Steeve walks through what training and inference actually involve from an engineering and industrial perspective:

• why training is a one-off research effort, while inference is repeated endlessly

• why “more is better” applies to training, but becomes a liability in production

• why inference represents the vast majority of compute needs

• how cost, reliability and margins reshape technical decisions

• why operating systems at scale is fundamentally different from building models

This episode helps understand why the real challenge is no longer creating models, but running them reliably, efficiently and sustainably in production.

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