Row 42894

Row ID: 42894 | Dataset Entry | Axioma AXP Content Repository

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Maybe the key thing to know about ML systems is that the most important element of them is the way in which they interface with the real world. How does information get into the system, and how does the output of it get used?

If you spent $400k to set up a bunch of arbitrary or task-specific stuff that has no reusable components and isn't part of any broader strategy then maybe that wasn't the best possible use of money. Or maybe you can consider it to be the cost of training personnel and developing institutional know-how.

However, if you spent $400k to set up a flexible and robust way of getting information about the world and then using conclusions drawn from that information in order to automatically make decisions, then that might have been a great investment. Sure, maybe it'll take over 10 years to make back that investment with things in their current form, but if you set up the system well then you can expand the scope of its use or iterate on its core functionality (e.g. do modeling iterations), and in doing so you can accelerate the money it saves (or even makes).

This is really the secret sauce behind companies that make stupid amounts of money with this stuff, like facebook or google. They've set things up so that they can repeatedly tweak their system and directly measure the impacts of those tweaks. People doing ML at big internet companies can look at a dashboard and see in stark terms exactly how much money every experiment they do might make or cost if it is deployed on a larger scale.

FieldValue
text Maybe the key thing to know about ML systems is that the most important element of them is the way in which they interface with the real world. How does information get into the system, and how does the output of it get used? If you spent $400k to set up a bunch of arbitrary or task-specific stuff that has no reusable components and isn't part of any broader strategy then maybe that wasn't the best possible use of money. Or maybe you can consider it to be the cost of training personnel and deve…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-22
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url_encoded Z0FBQUFBQm5Lak9kZzBPVGRUaUFOcGlkWEY0azhUV3RkVnlmOVhSZDE4d1FNWExid0xzc2JZcEo0M2s5RUI5N0k5YzBWT01RcXZMaUpPQ2VscGJ4bUt3ZEU5UlZ4Q1RnUEo4VmFIY3l4TWhQMm14OXhaTGRJWV9GRGZ1MnhFYzFtMXhpYV94cVExZngzT3VIWVBkTmJzSXpPeTQxdXRkb21ueUJPTFhLTDk3cG9sZHFCSlN4VVZ6NTBqR1lHZ0Z6NXAtREJBeXJrZW5GeEtQY0RSMHlqb3VERy1yNHU5N3N1UT09

Raw Record

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  "text": "Maybe the key thing to know about ML systems is that the most important element of them is the way in which they interface with the real world. How does information get into the system, and how does the output of it get used?\n\nIf you spent $400k to set up a bunch of arbitrary or task-specific stuff that has no reusable components and isn't part of any broader strategy then maybe that wasn't the best possible use of money. Or maybe you can consider it to be the cost of training personnel and developing institutional know-how.\n\nHowever, if you spent $400k to set up a flexible and robust way of getting information about the world and then using conclusions drawn from that information in order to automatically make decisions, then that might have been a great investment. Sure, maybe it'll take over 10 years to make back that investment with things in their current form, but if you set up the system well then you can expand the scope of its use or iterate on its core functionality (e.g. do modeling iterations), and in doing so you can accelerate the money it saves (or even makes).\n\nThis is really the secret sauce behind companies that make stupid amounts of money with this stuff, like facebook or google. They've set things up so that they can repeatedly tweak their system and directly measure the impacts of those tweaks. People doing ML at big internet companies can look at a dashboard and see in stark terms exactly how much money every experiment they do might make or cost if it is deployed on a larger scale.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
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