I've worked on ML platforms and systems for 9.5 years at every scale. The material looks great.
What would you recommend for a backend developer looking to make the switch from rest crud apps to ml platforms?
Machine Learning in Production (CMU Course)
11–20 of 39 posts
Re: Machine Learning in Production (CMU Course)
#12Can anyone sign up or do we have to get accepted into one of the top computer science programs in the country?
I believe in you! (I'm one of the instructors of the course :) )
Re: Machine Learning in Production (CMU Course)
#13I like the idea of learning a single “Kubernetis”
Re: Machine Learning in Production (CMU Course)
#14Fascinating; I just looked through the labs, and as a fullstack developer without that much experience in LLMs, it looks like I'm already closely familiar with half of them (git, flask, kafka, kubernetes) and the other half is just... code. No crazy math that I've come to associate with ML.
Does it mean that ML ops is a field that's actually not that hard to approach for a regular developer without a PhD?
Re: Machine Learning in Production (CMU Course)
#15Fascinating; I just looked through the labs, and as a fullstack developer without that much experience in LLMs, it looks like I'm already closely familiar with half of them (git, flask, kafka, kubernetes) and the other half is just... code. No crazy math that I've come to associate with ML. Does it mean that ML ops is a field that's actually not that hard to approach for a regular developer without a PhD?
the PhD requirement is either for actual research positions or a gatekeeping function to help companies narrow down their massive candidate pools
Re: Machine Learning in Production (CMU Course)
#16Is there somewhere I could follow along with other non students?
Re: Machine Learning in Production (CMU Course)
#17Fascinating; I just looked through the labs, and as a fullstack developer without that much experience in LLMs, it looks like I'm already closely familiar with half of them (git, flask, kafka, kubernetes) and the other half is just... code. No crazy math that I've come to associate with ML. Does it mean that ML ops is a field that's actually not that hard to approach for a regular developer without a PhD?
You can do a lot of work on MLOps and get very far without knowing much about ML. In a team with a senior ML engineers you are helping them scale and build stuff.
Like say you want to generate tons of synthetic data using simulations, you are likely to be more interested in questions say of batching, encoding formats, data loading etc than the actual process of generating unbiased data sets
If you need to collect and sample data from crowd sourcing, you likely need to know less about reservoir sampling than say figure out how to do it, online so it's fast or be efficient with $$$/compute spent on implementing the solution etc.
Re: Machine Learning in Production (CMU Course)
#18Fascinating; I just looked through the labs, and as a fullstack developer without that much experience in LLMs, it looks like I'm already closely familiar with half of them (git, flask, kafka, kubernetes) and the other half is just... code. No crazy math that I've come to associate with ML. Does it mean that ML ops is a field that's actually not that hard to approach for a regular developer without a PhD?
I don't think "Ops" roles generally require a PhD.
Re: Machine Learning in Production (CMU Course)
#19Does anyone know about literature or courses regarding building machine learning cluster infrastructure? I am mainly interested in building and scaling up the storage infrastructure, networking and scheduling approaches.
Re: Machine Learning in Production (CMU Course)
#20Fascinating; I just looked through the labs, and as a fullstack developer without that much experience in LLMs, it looks like I'm already closely familiar with half of them (git, flask, kafka, kubernetes) and the other half is just... code. No crazy math that I've come to associate with ML. Does it mean that ML ops is a field that's actually not that hard to approach for a regular developer without a PhD?
You can do a lot of work on MLOps and get very far without knowing much about ML. In a team with a senior ML engineers you are helping them scale and build stuff. Like say you want to generate tons of synthetic data using simulations, you are likely to be more interested in questions say of batching, encoding formats, data loading etc than the actual process of generating unbiased data sets If you need to collect and…
Reservoir sampling as in the stuff that's used in ReSTIR for graphics?
It's funny to me where statistics ends up sometimes.