Row 10187

Row ID: 10187 | Dataset Entry | Axioma AXP Content Repository

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There are different types of machine learning. For most of it, you won't need such a high spec machine. Most of it involves building predictive models on small datasets of just a few thousand rows. This is especially true when you're learning. So you don't need a particularly good laptop to do that. I had a Macbook Air with an Intel chip and 16Gb of RAM, and it got me through almost every exercise and hackathon I did while studying a Masters of Data Science.

However, there will be times where you need something much more powerful. For example, when you want to fine tune an LLM, or chain several of them together to do something crazy like build a model that converts sign-language to text in real time. When you do those sorts of projects, a Macbook M3 Pro with 36GB won't be enough. At that point, you'll need to use a cloud computing service from a company like AWS or Microsoft or Google Cloud. But that's good! When you get into the job market, one of the questions people will ask you is: What experience do you have with cloud providers? It will be good if you can point to projects you've built using them. And all you need to run cloud services is any old laptop with a browser, as all the actual computation will be done using the provider's computer.

And because of that, I'd say your most important priority when looking for a laptop is that it has a nice big screen, a good keyboard, and isn't very heavy. That way, you'll have plenty of screen real estate to watch training videos (with a bit of room for your IDE on the side). When you need a high spec computer, use cloud services. When you don't (which will be most of the time), the specs you get won't matter.

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Pro Tip 1: Look around for the Github Student Pack. It gives heavy discounts and free trials on a whole range of software, online courses, & cloud services, for anyone with a .edu email address.

Pro Tip 2: Re Mac vs Windows, it used to be that Mac was the obvious choice for data scientists. Not just because of the Mac build quality, but because Macs run on Linux, and so do the cloud providers. If you have a Mac, you're going to use the Terminal window and get used to all the Linux commands that will be useful as a cloud engineer. Having said that, you can now get a Terminal window that runs Linux on the Windows OS as well, so if you have to use Windows you can.

FieldValue
text There are different types of machine learning. For most of it, you won't need such a high spec machine. Most of it involves building predictive models on small datasets of just a few thousand rows. This is especially true when you're learning. So you don't need a particularly good laptop to do that. I had a Macbook Air with an Intel chip and 16Gb of RAM, and it got me through almost every exercise and hackathon I did while studying a Masters of Data Science. However, there will be times wher…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
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Raw Record

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  "text": "There are different types of machine learning. For most of it, you won't need such a high spec machine. Most of it involves building predictive models on small datasets of just a few thousand rows. This is especially true when you're learning. So you don't need a particularly good laptop to do that. I had a Macbook Air with an Intel chip and 16Gb of RAM, and it got me through almost every exercise and hackathon I did while studying a Masters of Data Science.   \n\nHowever, there will be times where you need something much more powerful. For example, when you want to fine tune an LLM, or chain several of them together to do something crazy like build a model that converts sign-language to text in real time. When you do those sorts of projects, a Macbook M3 Pro with 36GB won't be enough. At that point, you'll need to use a cloud computing service from a company like AWS or Microsoft or Google Cloud. But that's good! When you get into the job market, one of the questions people will ask you is: What experience do you have with cloud providers? It will be good if you can point to projects you've built using them. And all you need to run cloud services is any old laptop with a browser, as all the actual computation will be done using the provider's computer.  \n\nAnd because of that, I'd say your most important priority when looking for a laptop is that it has a nice big screen, a good keyboard, and isn't very heavy. That way, you'll have plenty of screen real estate to watch training videos (with a bit of room for your IDE on the side). When you need a high spec computer, use cloud services. When you don't (which will be most of the time), the specs you get won't matter.    \n\n----\n\nPro Tip 1: Look around for the Github Student Pack. It gives heavy discounts and free trials on a whole range of software, online courses, & cloud services, for anyone with a .edu email address. \n\nPro Tip 2: Re Mac vs Windows, it used to be that Mac was the obvious choice for data scientists. Not just because of the Mac build quality, but because Macs run on Linux, and so do the cloud providers. If you have a Mac, you're going to use the Terminal window and get used to all the Linux commands that will be useful as a cloud engineer. Having said that, you can now get a Terminal window that runs Linux on the Windows OS as well, so if you have to use Windows you can.",
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