<story><title>Ask HN: In 2022, what is the proper way to get into machine/deep learning?</title><text>By getting into machine or deep learning I mean building upto a stage to do ML&amp;#x2F;DL research. Applied research or core theory of ML&amp;#x2F;DL research. Ofcourse, the path to both will quite different.&lt;p&gt;Standing in 2022, what are the best resources for a CS student&amp;#x2F;decent programmer to get into the field of ML and DL on their own. Resources can be both books or public courses.&lt;p&gt;The target ability:&lt;p&gt;1. To understand the theory behind the algorithms&lt;p&gt;2. To implement an algorithm on a dataset of choice. (Data cleaning and management should also be learned)&lt;p&gt;3. Read research publications and try to implement them.</text></story><parent_chain><item><author>wokwokwok</author><text>Someone who can engineer infrastructure, pipelines and fire fight production issues &lt;i&gt;is&lt;/i&gt; hard to find, but that’s not the point I was making.&lt;p&gt;My apologies; It is real work; the point I was making is it’s not &lt;i&gt;ML work&lt;/i&gt;, any more than writing a yaml file is ML work.&lt;p&gt;If you want to write yaml files, any number of possibilities exist.&lt;p&gt;If you want to work with machine learning, then don’t become a data engineer. The skills are, mostly, not related ML, and more closely aligned with SRE &amp;#x2F; devops.&lt;p&gt;It’s not infrastructure and helping build models as you mature and advance: it’s almost literally just infrastructure and fire fighting… in my, limited, 3 years of experience as such.</text></item><item><author>robertlagrant</author><text>&amp;gt; It’s IT support for the people doing real work.&lt;p&gt;This is an appalling perspective. Good MLE skills seem a lot harder to find that good ML ones.</text></item><item><author>wokwokwok</author><text>&amp;gt; I think it can yield the highest success rate.&lt;p&gt;At what?&lt;p&gt;If you want to be an SRE for a data platform, sure, but this pretty thankless work:&lt;p&gt;- cleaning up dodgy data&lt;p&gt;- cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI).&lt;p&gt;- cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to “productionise” them.&lt;p&gt;- cleaning up behind brain dead scheduling systems that fail unexpectedly.&lt;p&gt;- constant churn with partners and cloud products for whatever the latest hotness is.&lt;p&gt;If you want to be solving actual problems with ML, this is a dead end.&lt;p&gt;It’s IT support for the people doing real work.&lt;p&gt;…so, it depends on your goals. Getting a job? Sure! Everyone wants a workhorse who they can dump all the annoying problematic on-call tasks to.&lt;p&gt;Learning ML, contributing to research, building models?&lt;p&gt;This isn’t a path that leads there.</text></item><item><author>santiagobasulto</author><text>Can I suggest a longer, but (I think) better route?&lt;p&gt;Try the Data&amp;#x2F;ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer.&lt;p&gt;There are a lot of people in the “pure” ML space, people with science background, with phDs, etc.&lt;p&gt;But there’s not enough people to support them: taking their models to producing, building their pipelines, etc.&lt;p&gt;If you get into Data&amp;#x2F;ML engineer, you’ll be working with these people and learning from them.&lt;p&gt;It’s a longer route for sure, but I think it can yield the highest success rate.</text></item></parent_chain><comment><author>lmeyerov</author><text>What?&lt;p&gt;A lot of the hard part isn&amp;#x27;t the model, and especially in a world where bert, xgboost, optuna, pytorch, etc have solved much of the classic problem and forced &amp;#x27;real&amp;#x27; DS to specialize on either the business consulting side (not math&amp;#x2F;engineering) or theory side (barely implemented). The rebrand of &amp;#x27;data analyst&amp;#x27; (SQL, powerbi, . ..) to &amp;#x27;data scientist&amp;#x27; by even top tech companies underscores this. It&amp;#x27;s not yet to where web dev has gotten in terms of global $20&amp;#x2F;hr fiverrr contractors, but already at say $40&amp;#x2F;hr for someone who can build real production models for more boring scenarios.&lt;p&gt;The result is the vast bulk of data scientists (phd, self-trained, consulting, ...) we interview are weak engineers, so going from a make-believe notebook to a trickier production scenario requires the data engineer &amp;#x2F; MLOps &amp;#x2F; etc to solve a lot that a typical DS doesn&amp;#x27;t really understand in practice. Scale, latency, distributed systems, testing, etc.  Likewise, the part the DS solves has little to do with the latest neuroips paper, and more just about lifecycle tasks like getting better data, which the other folks on the team will often be involved with as well.&lt;p&gt;So 2 natural high-paying paths here:&lt;p&gt;data engineer &amp;#x2F; MLOps -&amp;gt; MLEngineer -&amp;gt; DS&lt;p&gt;data engineer -&amp;gt; all-in-one data analyst&amp;#x2F;scientist -&amp;gt; ML&amp;#x2F;AI data scientist</text></comment>
<story><title>Ask HN: In 2022, what is the proper way to get into machine/deep learning?</title><text>By getting into machine or deep learning I mean building upto a stage to do ML&amp;#x2F;DL research. Applied research or core theory of ML&amp;#x2F;DL research. Ofcourse, the path to both will quite different.&lt;p&gt;Standing in 2022, what are the best resources for a CS student&amp;#x2F;decent programmer to get into the field of ML and DL on their own. Resources can be both books or public courses.&lt;p&gt;The target ability:&lt;p&gt;1. To understand the theory behind the algorithms&lt;p&gt;2. To implement an algorithm on a dataset of choice. (Data cleaning and management should also be learned)&lt;p&gt;3. Read research publications and try to implement them.</text></story><parent_chain><item><author>wokwokwok</author><text>Someone who can engineer infrastructure, pipelines and fire fight production issues &lt;i&gt;is&lt;/i&gt; hard to find, but that’s not the point I was making.&lt;p&gt;My apologies; It is real work; the point I was making is it’s not &lt;i&gt;ML work&lt;/i&gt;, any more than writing a yaml file is ML work.&lt;p&gt;If you want to write yaml files, any number of possibilities exist.&lt;p&gt;If you want to work with machine learning, then don’t become a data engineer. The skills are, mostly, not related ML, and more closely aligned with SRE &amp;#x2F; devops.&lt;p&gt;It’s not infrastructure and helping build models as you mature and advance: it’s almost literally just infrastructure and fire fighting… in my, limited, 3 years of experience as such.</text></item><item><author>robertlagrant</author><text>&amp;gt; It’s IT support for the people doing real work.&lt;p&gt;This is an appalling perspective. Good MLE skills seem a lot harder to find that good ML ones.</text></item><item><author>wokwokwok</author><text>&amp;gt; I think it can yield the highest success rate.&lt;p&gt;At what?&lt;p&gt;If you want to be an SRE for a data platform, sure, but this pretty thankless work:&lt;p&gt;- cleaning up dodgy data&lt;p&gt;- cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI).&lt;p&gt;- cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to “productionise” them.&lt;p&gt;- cleaning up behind brain dead scheduling systems that fail unexpectedly.&lt;p&gt;- constant churn with partners and cloud products for whatever the latest hotness is.&lt;p&gt;If you want to be solving actual problems with ML, this is a dead end.&lt;p&gt;It’s IT support for the people doing real work.&lt;p&gt;…so, it depends on your goals. Getting a job? Sure! Everyone wants a workhorse who they can dump all the annoying problematic on-call tasks to.&lt;p&gt;Learning ML, contributing to research, building models?&lt;p&gt;This isn’t a path that leads there.</text></item><item><author>santiagobasulto</author><text>Can I suggest a longer, but (I think) better route?&lt;p&gt;Try the Data&amp;#x2F;ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer.&lt;p&gt;There are a lot of people in the “pure” ML space, people with science background, with phDs, etc.&lt;p&gt;But there’s not enough people to support them: taking their models to producing, building their pipelines, etc.&lt;p&gt;If you get into Data&amp;#x2F;ML engineer, you’ll be working with these people and learning from them.&lt;p&gt;It’s a longer route for sure, but I think it can yield the highest success rate.</text></item></parent_chain><comment><author>achileas</author><text>I first had the DE title 7 years ago (going into it having never heard of DE), and have been doing MLE&amp;#x2F;platform work for the past 5. You’re projecting your limited experience onto a poorly defined role that varies wildly from company to company. My experience is much different from yours: little firefighting, lots of actual building. Yes there is infrastructure, but any good programmer these days should be able to stand up some basic infrastructure.&lt;p&gt;Yes, don’t get into it if you want to do ML research or apply ML, but if you are interested a bit in it and find building models the least creative, most boring shit ever like I do, and prefer traditional coding, it’s a nice spot to be in.</text></comment>