I enthusiastically read Kush Varshney’s book when it was released for free to the world several months back. Trustworthy Machine Learning offers a concise and clear overview of many of the ways that machine learning can go wrong, and so I was especially keen to get Kush on to talk more about his work and research.

I also got a stronger sense and appreciation for how good MLOps practices and workflows offered a clear path to ensuring that your machine learning models and behaviors could become more trustworthy. Kush has done a lot of interesting work, particularly with the AI Fairness 360 and AI Explainability 360 toolkits that I’m sure listeners of this podcast would find worth checking out.

In this clip, Kush thinks through the key roles that will likely remain following an automation away of certain tasks:

Trustworthy Machine Learning is available to read for free at the website linked and I would recommend you give it a read.

As always, full show notes and links are available on our dedicated podcast page.


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