1) Depend on your own goal. Its wise if you believe in the future, market appreciate ML eng higher than software eng (within same years exp)
2) Yes. I did (> 3 years now)
3) Start find the ML job
Is there an ML engineering practice that isn't focused on building models but more on managing/deploying/scaling models?
Yes. Companies who adopt ML at large scale usually need this.
i.e. can I avoid learning all the maths underneath?
Yes its possible as long as you are focusing on infrastructure. Eg: create/monitoring pipelines, models, etc.
Noted: While you finding job, try to take professional ML engineering certification/program (Google, AWS, Azure, etc) - they will provide you hands-on lab experience with some case studies. The cost is super cheap
I'm building a platform where product managers and engineers can build interaction automation with users using small model. The goal is to help people to build LLM for them without deep expertise in DS/ML, train and host the model in their infrastructure, where no data require to be submitted.
Depend on use case. For me, its very crucial. Context, I'm working on ML and run several processing where usually consume memory up to 1TB at my workstation. Since my RAM only 196GB, this will trigger OOM killer, without Swapfile.
I use HHHL PCI SSD to host my swapfile to withstand with rapid read-write activities
Global distribution is complex. So there is no easy way to do it. Now, what is the option here? Your assumption, around 6 billion of those humans have a bank account. So, we can leverage the bank network to do the distribution.
There is a chance of the same person opening multiple account. Integrating with one bank at a time will help not to do manual transfer and build a centralized database for verification simultaneously. There is no time limit here, I assume this is fine.
How about the rest, the unbankable? Via country social aid. There is a high correlation that the unbankable is similar to under poverty population. With social aid, it will reach them. Cross-check with the central database for verification.
Now, what if the 1 dollar doesn't need in the form of physical money? There are so many alternatives to this. I said, follow on how people spend money.
You can use it to pay each Taxpayer all countries, pay the debt of each person in the bank, and subsidize commodities that the unbankable population consume: data, rice, bread, electricity, vaccine and others thing that people spend. So, understand how unbankable people spend and receive money.
There is a cost of distribution. My rough intuition is probably around 30-40%, if we are looking for precise in-person equal distribution. Yes, that is really expensive, even more for unbankable, because most of them will receive cash.
Thank you for the question @paulluuk. This is a good brain exercise and seems like consulting interview question for me hahaha
Hey OP, I feel relate to your problem as CTO of a startup that is growing fast and requires heavy integration with operation and biz (Seed to Series B in 4 years). My day to day is quite hectic, however, I'm happy to do experiments whether I can contribute or help.
My total experience is 14 years start from eng, project manager, CTO and now handling Product. I have experience managing talents from Asia, India, and Silicon Valley. I spend my time weekly aligning with the biz stakeholders like VP of Operation, Finance, Commercial, HR and of course, my co-founder the CEO :)
You can reach me out at barudevice0 @ Gmail dot com
Hello, I have more than a decade experiences in startups: small, medium and big. If i can wrap it up, think that startup provides the best slingshot to your life journey if you ride the momentum in the right time.
There no time standard whether one, two or six years is the right amount to spend in a single startup. All you need is see your growth as your indicator. If your startup revenue growth is linear, but your own personal growth is sky-rocketing, that's mean you should stay.
The company will have exponential growth if the team inside grow in explosive way.
I've spent roughly 3-4 years working experience on Django + Python in total. I've switched to Elixir / Phoenix since 2 years ago. I can say, the two biggest cons (despite is many cons) is the packages maturity and doing heavy lifting processing.
The first one, I can't complaint. Every programming language have it's weakness, to solve that I'm offload processing stuff into Rust. Luckily, Elixir have NIF integration with Rust.
I have made long research and comparison about this. My budget was $1200 usd. My need basically browsing, sending email, write docs and coding. Nothing fancy and i have small working room.
Finally, i bought 38inch curve ultrawide (was huge 25% discount). I found it amazing and been using it for 1 year now. I have 34inch flat screen at office, but i prefer 38inch.
I tried more than 40 inch screen at office but i got neck problem for that.
This is awesome! Never know if anyone build things like Segment. I will try this alternative solution to our company and will keep update here about the result.
1) Depend on your own goal. Its wise if you believe in the future, market appreciate ML eng higher than software eng (within same years exp)
2) Yes. I did (> 3 years now)
3) Start find the ML job
Is there an ML engineering practice that isn't focused on building models but more on managing/deploying/scaling models?
Yes. Companies who adopt ML at large scale usually need this.
i.e. can I avoid learning all the maths underneath?
Yes its possible as long as you are focusing on infrastructure. Eg: create/monitoring pipelines, models, etc.
Noted: While you finding job, try to take professional ML engineering certification/program (Google, AWS, Azure, etc) - they will provide you hands-on lab experience with some case studies. The cost is super cheap