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I understood their example more like: automating the scaling of servers is easy, having proper inputs for this scaling to be reliable is hard. What they propos

I understood their example more like: automating the scaling of servers is easy, having proper inputs for this scaling to be reliable is hard. What they propose is to lend weeks if engineering time to perform analysis in the hope to find some relatable issues. Are both this engineering time and the issues fixing time relevant for non faang companies? In other words: The lever effect of not having issues is fewer, so the rentability of such analysis decrease. Where does the rentability become negative?

mgaunard 2y ago
In practice it's pretty much impossible to get precise requirements without automatically learning them from how the application performed in the past. The problem is that it is high-risk to automatically perform those changes since they might affect the application in ways you do not expect.
- n0n0n4t0r 2y ago
  I really don't think they are talking about requirements, at least not specifically. Aren't you focusing on your own level issues?
  - mgaunard 2y ago
    the example they gave is the quota rightsizer. Its job is to infer the right quota (requirement allocation).
    - n0n0n4t0r 2y ago
      Yes, but I mean that they shifted the focus to the input measurements over correct quota's value.