June 26, 2019
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4. building (or renting) adequate ML infrastructure
5. scaling to meet ML requirements
6. enabling smooth deployment of ML projects

Unfortunately, sometimes the proposed solutions highlight the terrible mindset of today’s approach to IT. An example (related to problem 1):


This is one of the many thoughts I post on Twitter on daily basis. They span many disciplines, including art, artificial intelligence, automation, behavioral economics, cloud computing, cognitive psychology, enterprise management, finance, leadership, marketing, neuroscience, startups, and venture capital.

I archive all my tweets here.