This week I spoke with Tristan Zajonc, the CEO and cofounder of Continual, a company that provides an AI layer for enterprise companies or, as we’ll get into in the podcast, the so-called ‘modern data stack’.

He previously worked at Cloudera as a CTO for machine learning and as the head of the data science platform there, and he holds a PhD in public policy from Harvard University.

In our conversation we discussed the different levels of abstraction one can take when dealing with the MLOps problem. We spoke about all the different ways that machine learning can fail in production settings and of course we discussed the concept of the ‘modern data stack’ and what that means.

In this clip, Tristan outlines a vision of where machine learning is headed:

We explored the ‘modern data stack’ and the universe of ‘enterprise’ machine learning tools, which was definitely a blind spot of mine. Check out the full episode below or however you prefer to listen to podcasts! As always, full show notes and links are available on our dedicated podcast page.


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