I think we may be talking to different customers. I talked with ~200 customers last year, and the most common question was "What do I use ML for?" and the secon

I think we may be talking to different customers. I talked with ~200 customers last year, and the most common question was "What do I use ML for?" and the second most common was "How do I get started?" Put another way, the average customer has ~zero ML usage today. I'd guess that 95%+ of all businesses have ML usage today. Further guessing would say that <1% of ML usage actually care about levels of accuracy beyond "it's better than the hacked together set of rules/filters I use today." These are very large businesses with lots of money to spend on a solution. There are many ways to measure "better", and AutoML does apply here. This includes "better == faster to train or develop" [1], "better == you need less data" [2], "better == lower error rates"[3]. While I agree that many of measures do not apply across datasets, most customers only have one dataset per problem. [1] Predictive accuracy and run-time - https://repositorio-aberto.up.pt/bitstream/10216/104210/2/192541.pdf https://repositorio-aberto.up.pt/bitstream/10216/104210/2/19... [2] Less data - https://arxiv.org/abs/1703.03400 https://arxiv.org/abs/1703.03400 [3] https://link.springer.com/article/10.1007/s10994-017-5687-8 https://link.springer.com/article/10.1007/s10994-017-5687-8 Disclosure: I work at Google on Kubeflow