Row 44081

Row ID: 44081 | Dataset Entry | Axioma AXP Content Repository

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it depends. I've done large scale computer vision projects that had terrible ROI, and I've made a small model for deciding whether customers are trustworthy enough to be allowed to pay later that the customer said would increase their profit by 6 million per year, on a 25k project cost. one of the ways to mitigate risk is starting small, with a POC and MVP phase, so you can get an idea of what performance you can expect, and what the operational costs are. this helps a lot when trying to estimate your ROI.

ML/DS can be quite experimental, and the ROI depends on how the model performs, if it works at all. can I ask why the costs for implementation/operation are so high? are you running very large DL models on live inference? why did it take a large team to get one model into production? can you repurpose parts of the code/models for other projects?

is this a product you could sell to other companies? one of my previous employers would regularly partner up with a company to build an ML solution, and then use the client's contacts to sell the same solution to other companies. the original project might not get the greatest returns, but if you can resell it to more customers your extra development costs are generally quite low, so it's way easier to turn a profit.

FieldValue
text it depends. I've done large scale computer vision projects that had terrible ROI, and I've made a small model for deciding whether customers are trustworthy enough to be allowed to pay later that the customer said would increase their profit by 6 million per year, on a 25k project cost. one of the ways to mitigate risk is starting small, with a POC and MVP phase, so you can get an idea of what performance you can expect, and what the operational costs are. this helps a lot when trying to estimat…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-22
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{
  "text": "it depends. I've done large scale computer vision projects that had terrible ROI, and I've made a small model for deciding whether customers are trustworthy enough to be allowed to pay later that the customer said would increase their profit by 6 million per year, on a 25k project cost. one of the ways to mitigate risk is starting small, with a POC and MVP phase, so you can get an idea of what performance you can expect, and what the operational costs are. this helps a lot when trying to estimate your ROI.\n\nML/DS can be quite experimental, and the ROI depends on how the model performs, if it works at all. can I ask why the costs for implementation/operation are so high? are you running very large DL models on live inference? why did it take a large team to get one model into production? can you repurpose parts of the code/models for other projects? \n\nis this a product you could sell to other companies? one of my previous employers would regularly partner up with a company to build an ML solution, and then use the client's contacts to sell the same solution to other companies. the original project might not get the greatest returns, but if you can resell it to more customers your extra development costs are generally quite low, so it's way easier to turn a profit.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1PUXU3b0hkcF9xZ0pmdEo0RUlPTHU1S1dJY0psNGFjVDVkNW04YjNZRlZoY0puNExXYnVRVm1Iajd1djI5cWliMU5YUmNBOEkxa3RFV09VbmJRZXZaaGc9PQ==",
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}
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