If you're buying more than one, that'll easily cover enough to cable up whatever network you want between them.
Many colo providers also have their own cloud services, and nothing stops you from using S3, SQS and the like, though of course if you use AWS services "from the outside" you do need to factor in their extortionate outbound bandwidth charges.
Or like saying that if you spend 20 hours every day in an Uber buying a car would be cheaper over 3 year term.
Nobody is disputing that buying similarly specced hardware on the market is likely to have a cheaper TCO, the point of cloud providers is that also have a bunch of stuff attached to the servers, which turns to be pretty important.
Where do I get GMAIL for my Raspberry Pi? If you order that Lambda server, you get the same good Linux interface you'll get over AWS. In fact they actually seems to offer some kind of software package that you wouldn't get over AWS.
Sure AWS has value-added, but in this case, it's mostly being able to not being billed for unused hours (what we call scalability), which for many is certainly worthwhile, but that's all. Is there anything I'm missing that AWS offer?
> if you spend 20 hours every day in an Uber buying a car would be cheaper over 3 year term.
Isn't that the case? I failed to see what's wrong in that sentence. I don't own a car, I subscribe to a service similar to Car2Go, I'm at about 150$ a month right now. Sure there's value added there, but owning one also has value added, just different ones and at one point it will be more worthwhile to own one.
As bubblethink already mentioned, the article is arguing that batch processing jobs (e.g. those characteristic of Deep Learning) don't benefit substantially from cloud infrastructure.
High availability, edge processing, fast network upload, and the lego blocks for creating redundancy are moot.
Most training algorithms automatically recover if a node goes down. That covers all required failure handling.