Your AI skills are worth less than you think
Stopped reading at &quot;manual tuning is going the way of dodo, and good riddance&quot;. The guy clearly has no idea whatsoever what he&#x27;s talking about (figures: he was an SRE at Google, nowhere close to any ML&#x2F;DL efforts).<p>Hyperparameter tuning is an area of active research and a dark art of sorts, and it will remain that for the foreseeable future for one simple reason: hyperparameters are interdependent and _data dependent_ as well, and nobody, not even Google with its TPUs has the compute to tune them &quot;automatically&quot; to any meaningful extent every time. What you see with the various &quot;automl&quot; efforts is merely transfer learning, refining of existing models on customer&#x27;s data. You can learn how to do that in one evening, with no prior DL experience if you&#x27;re a good coder, and in 2-3 evenings if you aren&#x27;t a good coder. Point is, it&#x27;s not a difficult problem, just follow a tutorial.<p>That&#x27;s great, that&#x27;s how smart researchers build practical models, but that&#x27;s not really &quot;auto-ml&quot;, in the sense that there&#x27;s no hyperparameter tuning going on (as far as I know) and no new problems are being solved. It&#x27;s just a classifier: anybody can do that with common off the shelf tools and pre-trained checkpoints.<p>Now achieving state of the art results, solving novel tasks for which there aren&#x27;t any ready-made solutions, doing DL efficiently, figuring out creative ways to get labeled data, semi-supervised or few-shot methods, etc, that&#x27;s where it&#x27;s at right now. And the comp has never been better if you know what you&#x27;re doing.