Row 46324

Row ID: 46324 | Dataset Entry | Axioma AXP Content Repository

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What happened? I feel like even without ML knowledge it can be straightforward to estimate budget and ROI of a project. Even if you have no such estimation, maybe look into other ways to cheaply/quickly test the hypothesis. Look into setting up AB tests.

Edit: I didn’t really answer your questions so here goes. I work mostly on recommendations, risk/fraud detection, credit sevices domains. All of my projects fail the first AB test. Literally nothing works as well as human or a good heuristic. It usually takes a few “rounds” to get it right depending on the specific thing. We always have to be mindful about cost (computing, storage etc) and efficiency. No project is greenlighted without a proper hypothesis validation first, and a solid budget/benefit/plan presented. For startups it’s obviously much easier but we don’t have to pay much anyway due to small data.

FieldValue
text What happened? I feel like even without ML knowledge it can be straightforward to estimate budget and ROI of a project. Even if you have no such estimation, maybe look into other ways to cheaply/quickly test the hypothesis. Look into setting up AB tests. Edit: I didn’t really answer your questions so here goes. I work mostly on recommendations, risk/fraud detection, credit sevices domains. All of my projects fail the first AB test. Literally nothing works as well as human or a good heuristic. I…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-22
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Raw Record

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  "text": "What happened? I feel like even without ML knowledge it can be straightforward to estimate budget and ROI of a project. Even if you have no such estimation, maybe look into other ways to cheaply/quickly test the hypothesis. Look into setting up AB tests.\n\nEdit: I didn’t really answer your questions so here goes. I work mostly on recommendations, risk/fraud detection, credit sevices domains. All of my projects fail the first AB test. Literally nothing works as well as human or a good heuristic. It usually takes a few “rounds” to get it right depending on the specific thing. We always have to be mindful about cost (computing, storage etc) and efficiency. No project is greenlighted without a proper hypothesis validation first, and a solid budget/benefit/plan presented. For startups it’s obviously much easier but we don’t have to pay much anyway due to small data.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
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