Row 33113

Row ID: 33113 | Dataset Entry | Axioma AXP Content Repository

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I'm not familiar enough with the field to make a lot of specific recommendations, but quite a lot of people in scientific HPC are doing machine learning now. I think all the big areas of scientific HPC are doing this, e.g. weather simulation, semiconductor and condensed matter simulation, etc. Alphafold3 and similar things are another example.

HPC/distributed/cloud computing comes in a lot of different flavors though. They're all sort of trying to accomplish the same thing, but the requirements vary a lot. Scientific HPC emphasizes fast, low latency, synchronized computation. Industry cloud/distributed computing for e.g. ranking engines is sort of the opposite, where a lot of computations can be slow or asynchronous, and often you have disparate processes chained together through intermediate datasets/outputs. LLMs can be somewhere in between, where you need sophisticated high performance compute for inference but also you might be drawing on results from a database that were created by slower processes elsewhere.

If you love MLops you can also just go make a bunch of money doing nothing but that. Most people want to do sexy modelling stuff, especially academics, but the real heroes are the infrastructure people. Someone who understands the modelling well and who can also make a big system that actually works is not going to have trouble getting a good job. That's basically just what ML engineers do in industry.

TLDR there's a ton of opportunity to combine your interests, you just sort of have to narrow down the scope of your interests more.

FieldValue
text I'm not familiar enough with the field to make a lot of specific recommendations, but quite a lot of people in scientific HPC are doing machine learning now. I think all the big areas of scientific HPC are doing this, e.g. weather simulation, semiconductor and condensed matter simulation, etc. Alphafold3 and similar things are another example. HPC/distributed/cloud computing comes in a lot of different flavors though. They're all sort of trying to accomplish the same thing, but the requirements…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-21
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Raw Record

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  "text": "I'm not familiar enough with the field to make a lot of specific recommendations, but quite a lot of people in scientific HPC are doing machine learning now. I think all the big areas of scientific HPC are doing this, e.g. weather simulation, semiconductor and condensed matter simulation, etc. Alphafold3 and similar things are another example.\n\nHPC/distributed/cloud computing comes in a lot of different flavors though. They're all sort of trying to accomplish the same thing, but the requirements vary a lot. Scientific HPC emphasizes fast, low latency, synchronized computation. Industry cloud/distributed computing for e.g. ranking engines is sort of the opposite, where a lot of computations can be slow or asynchronous, and often you have disparate processes chained together through intermediate datasets/outputs. LLMs can be somewhere in between, where you need sophisticated high performance compute for inference but also you might be drawing on results from a database that were created by slower processes elsewhere.\n\nIf you love MLops you can also just go make a bunch of money doing nothing but that. Most people want to do sexy modelling stuff, especially academics, but the real heroes are the infrastructure people. Someone who understands the modelling well and who can also make a big system that actually works is not going to have trouble getting a good job. That's basically just what ML engineers do in industry.\n\nTLDR there's a ton of opportunity to combine your interests, you just sort of have to narrow down the scope of your interests more.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-21",
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