The "Shadow predictions" bit is interesting. They could set that up in a way to score it versus actuals...where the shadow implementation automatically becomes the live one once it's better than the incumbent.
Sibyl, was also the name of the Delphi clone for OS/2 :)
I'm yet to have the 'wow' moment from any recommendation service.... Netflix, Amazon, Deliveroo... At best I get the 'meh why not'. It's obviously a hard problem to solve.
I see posts like this and just think that as far as I can tell DD's recommendations are:
Ads
Ads
Ads
Shitty place nobody would want to eat
Shitty place nobody would want to eat
Shitty place nobody would want to eat
Shitty place nobody would want to eat
Oh look something interestingI just cancelled my membership (was free with a credit card i already had).
They offered a "free membership" for 10 months "at a $120 value" (which used to be $60 a year), however I couldn't figure out how to activate it.
I can't figure out what the value is over other services/direct from restaurant. Like another poster, most of the suggestions were bad. "Do you want dairy queen?" Or "quick you can order free from Kwik Trip in the next 10 minutes".
The post makes the suggestions look good. My top recommendation is the cheesecake factory. Followed by McDonalds. Who are they targeting? Who is getting McDonalds delivered?
I predict they'll get my order wrong or leave something off despite charging me and deliver my food cold.
Wait, so they are actively encouraging Sybil attacks?
People are commenting on the quality of Door Dash predictions, but that isn’t what the article is about. The article is just about the architecture for high speed/throughput of prediction requests.
I would be interested in knowing how much improvement they saw by using C++ or Kotlin. Also, I don’t really understand what compute service is actually used to run the model predictions in this framework.