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Ask HN: In 2022, what is the proper way to get into machine/deep learning?

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Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#181

Post by ralston3 »

jedberg wrote: post #167
solardev wrote: post #165

Earlier quoted context omitted.

Could you please provide some examples of what a ML Ops person might do/manage in their day to day jobs?

- Tracking changes is different. Not only do you have to track code changes, but also training data and model changes. You need to build systems that allow for this change management. - Monitoring -- you have to build specialized monitoring to check for model degradations. Is the model still outputting valid/correct predictions? If not you need to roll back to an old model (see above). Those are the two main ones.

I would say this falls under #3 - ML engineer

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#182

Post by hansvm »

YetAnotherNick wrote: post #171
ford wrote: post #117

I work in ML - I might make 3 buckets for ML careers right now: 1. ML/DL Researcher 2. Data scientist - 20/80 engineering vs modelling 3. ML Eng - 50/50 (or 70/30) engineering vs modelling People suggesting working in engineering to support ML are right that there's a lot of demand, but it's not what you're asking for. Becoming an ML/DL researcher working on novel techniques or new models will be hard without academi…

> Becoming an ML/DL researcher working on novel techniques or new models will be hard without academic research experience This is not correct for current DL research. I know many undergrad engineers who wrote papers in top conferences. Current DL is mostly about implementing ideas, running experiments, having good sense of data etc. rather than theory. It's an open secret in DL that theories are just there to please…

I think you're talking past each other -- doing solid ML research vs being well paid as such a researcher.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#183

Post by kingcai »

My best advice is to find people who do what you want to do and try to learn as much as possible from them. If you're interested in doing ML/DL research I think the best way to get into the field is to reach out to professors. I studied ML/DL (books, projects, classes, reimplementing papers) for several years in undergrad, but discussing and debating ideas is the one thing that took my understanding to a much deeper level. A good professor will also point out gaps in your knowledge that you might be missing.

A second bit of advice: Programming (and execution) skills are IMO heavily undervalued by people looking to get into ML. The faster you can write code, debug, and implement new things, the easier it is to produce good research.

Some books I liked: PR & ML (Bishop), Deep Learning Book (Goodfellow), AI: A Modern Approach (Norving), Elements of Statistical Learning (Friedman)

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#184

Post by kingcai »

tunesmith wrote: post #166

I've thought about going in this direction multiple times, but I have this impression that it's pretty much unavoidable that you'd be working with annoying ETL processes and "cleaning data" (parsing, transforming from one set of columns to another, etc) over and over again. Is this true? I hate that stuff, but I'm particularly interested in graphical data structures and I even like probability and statistics and line…

It's an unavoidable part of the job, but it's definitely not most of the work.

I feel it's kind of like writing tests in SWE - something you do because it's beneficial even if it's not enjoyable.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#185

Post by ford »

YetAnotherNick wrote: post #171
ford wrote: post #117

I work in ML - I might make 3 buckets for ML careers right now: 1. ML/DL Researcher 2. Data scientist - 20/80 engineering vs modelling 3. ML Eng - 50/50 (or 70/30) engineering vs modelling People suggesting working in engineering to support ML are right that there's a lot of demand, but it's not what you're asking for. Becoming an ML/DL researcher working on novel techniques or new models will be hard without academi…

> Becoming an ML/DL researcher working on novel techniques or new models will be hard without academic research experience This is not correct for current DL research. I know many undergrad engineers who wrote papers in top conferences. Current DL is mostly about implementing ideas, running experiments, having good sense of data etc. rather than theory. It's an open secret in DL that theories are just there to please…

I didn't mean to imply it needed to be _graduate_ research - however it would be news to me if they were publishing at top conferences independently of a lab or research branch at a company.

How do the people you know go about it?

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#186

Post by ford »

bilsbie wrote: post #159
ford wrote: post #117

I work in ML - I might make 3 buckets for ML careers right now: 1. ML/DL Researcher 2. Data scientist - 20/80 engineering vs modelling 3. ML Eng - 50/50 (or 70/30) engineering vs modelling People suggesting working in engineering to support ML are right that there's a lot of demand, but it's not what you're asking for. Becoming an ML/DL researcher working on novel techniques or new models will be hard without academi…

Is it possible to do 3 but stick to coding and not touch any dev ops work? My nightmare is finding a role like that and realizing I’m just a dev ops guy.

If by devops you mean managing architecture + deployments vs only writing code, I imagine that exists at places with mature infrastructure, but honestly MLOps is developing so quickly that small to medium ML teams will go through some infrastructure churn.

The best advice I have is apply for jobs with descriptions that sound like what you want to do and clarify with the hiring manager/recruiter that it actually matches what you're looking for

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#187

Post by ford »

kwoff wrote: post #179
jvans wrote: post #147

Earlier quoted context omitted.

Richard McElreath's Statistical rethinking is an absolute masterpiece. https://xcelab.net/rm/statistical-rethinking/ on statistics

Thanks. BTW, I found there's a much cheaper ($80 -> $27) paperback version published a few days ago.

Can you share the link?

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#188

Post by mrcartmeneses »

badpun wrote: post #80
mrcartmeneses wrote: post #61

Earlier quoted context omitted.

I’ve kinda developed the view that large organisations come to mirror the Russian Communist Party. I’m interested in “flow capture based on power relationships”. Do you have any recommended reading on this?

> I’ve kinda developed the view that large organisations come to mirror the Russian Communist Party. Only the ones which have an unkillable cash cow. So, I suspect Google or large banks are mostly like that, but places like SpaceX or even large consulting firms (Delloitte, IBM etc., where managers essentially eat what they kill) cannot allow themselves to degenerate into a Chinese court.

Interesting you say that because there’s some critical analysis I’ve come across in the past that states that taxation is a key driver towards democracy and that captured wealth (such as from oil) promotes oligarchy and dictatorship. There are outliers in either direction, naturally.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#189

Post by mrcartmeneses »

psi75 wrote: post #101
bsenftner wrote: post #90

Earlier quoted context omitted.

Now this is interesting. I've always found it fascinating that when profit is on the table, democracy is nowhere to be found. I've looked, not too hard TBH, for essays and literature discussing the correlations to business model management structures and government/nation political hierarchies - not education level (propaganda), but critical analysis. I've been an employee of several of the top corporations on our pl…

Unfortunately, the only way to prevent hierarchy is to create a limited hierarchy (this is the purpose of constitutions) a priori; hierarchically naive organizations fail on this account. External parties will demand hierarchy simply because they want to know your organization (or nation) isn't wasting their time--no one wants to deliver a sales pitch to people who can't authorize purchases. If they're not careful, a…

Interesting take. In British history the first positive step towards freedom that I note was the creation of law courts. These gave surfs some power over their lords and provided some level of fairness rather than everything being about favour.

The US does seem to lag the UK and Europe in terms of employment law in some cases (no formal employment contracts for most employees, can be fired without notice, little statutory holiday, maternity or paternity leave entitlement, etc. etc.)

It has been argued that the union movement — while susceptible to corruption — was a hugely positive force in economic and political terms for American workers. Unfortunately thatcher and Reagan saw this as such a threat that they attempted to destroy their own manufacturing base in order to smash the unions.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#190

Post by mrcartmeneses »

jokethrowaway wrote: post #126
mrcartmeneses wrote: post #61

Earlier quoted context omitted.

I’ve kinda developed the view that large organisations come to mirror the Russian Communist Party. I’m interested in “flow capture based on power relationships”. Do you have any recommended reading on this?

Every large organisation tends to be like a small government. Inefficient, drown in politics and unable to change. There are exceptions - where someone principled dictator impose a VC style model where teams basically become independent startup and die or succeed. 100 fails, one becomes the next revenue maker for the company. That's how AWS was born.

There’s way less inefficiency and way more accountability in the public sector. Look at how efficient publicly funded schools are, for example, or publicly funded rail or healthcare. You could literally pick almost any industry.

Accountability comes from elections. If managers in companies had to be re-elected it would be interesting.

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