AI is everywhere.
Except in many enterprises.
Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail, and for the few that are ever deployed, it takes many months to do so. While AI has the potential to transform and boost businesses, the reality for many companies is that machine learning only ever drips red ink on the balance sheet.
In this talk, we shall explore why machine learning in the enterprise is so difficult and why it's not because of machine learning itself. We'll see why DevOps for ML (a.k.a. MLOps) is not enough. By reasoning from a high-level business target down to the technical requirements, we'll see why getting the infrastructure right is hard, but also why it need not be, and how you can still benefit from best-of-breed solutions in an all-in-one platform thanks to cloud-native technologies.
https://gateway.on24.com/wcc/eh/2010041/lp/2714579/d2iq-ai-chihuahua-why-machine-learning-is-dogged-by-failure-and-delays
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