Hey Jeremy, i just want to say that I love your course and the way you teach. I refer everyone to the Fast AI in my YouTube videos on getting started with machine learning. Please keep up the great work!
Sounds to me like you might grow to resent this individual if you gave them shares. I agree with everyone else: give them a % revshare for hitting milestones. No shares this late in the game.
Also, in this looming recession, equity will be less sexy to hold for employees. So use that to your advantage. Good luck!
I'm Poor Charlie's Almanac, Charlie Munger explains that cheating is a huge problem, and that if you create a game where people can cheat to get ahead, they almost inevitably will.
At Google you could cheat the promotion system. Just spend more time optimizing for promo than for improving products for your customers, and you would be handsomely rewarded. You end up with the cheaters becoming the leaders and the good engineers leaving in frustration when they need to take orders from cheaters.
I disagree. There is a ton of low hanging fruit in applying SOTA ML techniques to niche problems. It just requires a bit of business sense, but there is a lot of money to be made there. More than what I was making as someone doing ML research at Google Research.
The Deepmind lead behind AlphaStar, there StarCraft 2 bot that beat several world champs, once said, "the best way to solve a reinforcement learning problem is with supervised learning". That's because supervised learning is relatively extremely efficient, and just RL with more constraints.
Just start learning the basics of supervised learning for classification and regression on common benchmark tasks like CIFAR and UCI. Apply a mix of linear models, neural networks, and trees like random forests and GBDTs. Next try convolutional networks for vision and transformers for NLP. You'll be all set to solve most real world problems.
I've been working on imitation learning recently and can attest that reducing RL to supervised learning gives remarkable results, even when using naive algorithms (like Behavioral Cloning).
That's an excellent quote by the way, do you perhaps have any source? Sounds like a good opener slide :)
The more assumptions you relax, the more general the algorithms become, for example going from immediate reward to delayed reward means going from supervised to reinforcement learning.
The trade-off is the more general algorithms needs many times exponentially more data and compute to come to a similarly good solution.
That's why reinforcement learning has seen so practical few applications relative to supervised learning. There's no free lunch.
That said, as a ML practitioner I would love it if I could just apply a single master algorithm to all problems, but that is likely many years away.
At the same time, fine-tuning sample efficiency increases with scale, so at some point you can possibly one-shot learn state and get rid of exponential searches, solving NP-Hard problems with heuristics. Sounds like a free lunch to me. At least if you can afford a net large enough.
It sounds like the more general the algorithm, the more stateful it needs to be before it can be useful. On the other hand, specialized algorithms need less to zero state but have limited applications.
I've been working a Full Time Employee in the industry for over 8 years at multiple companies. Never once has HR done anything remotely useful to help me when there were family problems or tragedy, except maybe a couple weeks of paid leave.
But I would like to hear the take from a founder who built an HR team to know if maybe I am missing something.
I'm really curious if it's really different being an employee vs a contractor in that respect.
>> I've been working a Full Time Employee in the industry for over 8 years at multiple companies. Never once has HR done anything remotely useful to help me when there were family problems or tragedy, except maybe a couple weeks of paid leave.
I’m curious (genuinely) what the company could have done different for you in these cases? Personally I wouldn’t expect anything from the company other than paid leave/general empathy from managers with amount of leave varying depending on the loss (e.g. loss of a partner requiring more time than loss of a grandparent). I’m not sure what else I would want from my company or what they could offer.
I thought the same thing. The only thing I'd want from an employer would be time away and space to deal with things on my own or with family/friends. No texts, calls, emails about anything work related. If you have social connections at work, some show of support from colleagues (cards, flowers, calls, donations, etc) may be appropriate, but I would not expect that. Struggling to think of anything else most employers should be expected to do.
Our company offers its US employees access to a service called Wellthy. It is good to know that when you are dealing with issues in your personal life you can reach out to a dedicated, named individual that has expertise in the area you are struggling and that is there to help you navigate and organize. Even if it's just for the feeling that you are not alone.
Similar programs are available to employees in other countries.
Can someone please share the current state of deploying Pytorch models to productions? TensorFlow has TF serving which is excellent and scalable. Last I checked there wasn't a PyTorch equivalent.
I'm curious how these charts look for companies that are serving ML in production, not just research. Research is biased towards flexibility and ease of use, not necessarily scalability or having a production ecosystem.
There is TorchServe but I haven't used it so I'm not sure how production ready it is.
You have Nvidia's triton server which support cpu and gpu with tf1,tf2,pytorch,onnx and tensorRT.
You have onnx runtime which can run on cpu and gpu and there are convertors from tf and pytorch to onnx.
Then you have cloud based solutions like AWS sagemaker, elastic inference endpoints and even Inf1 instances that use AWS Inferentia chips which you would run with the Neuron SDK, they even have TensorFlow serving containers with built it support for Inferentia.
End of the day it really depends on your model, size, latency, inference runtime and the cost obviously.
And that's before optimizations like FP16, BFLOAT16, TF32, INT8, pruning, layers rewrite, getting rid of batch normalization etc.
Then you have up and coming solutions like Neural Magic (not associated) deepsparse to create sparse models for inference.
And that's just for cloud if you are talking about edge ml it's even more down the rabbit hole..
Not too late. You've probably derisked the product side a but since you already have years of experience building technical products. You just need to learn the basics of running a business now, and get comfortable with sales and marketing.
I'm 32 and just started a solopreneur bootstrapped business. I have much more confidence now than when I was in my 20s.
The biggest thing is that I'm more patient now. In your 20s, it's easy to feel like you need everything right now. But as you get older, you learn patience. I'm still impatient at times, but I also feel more comfortable saying I'll spend the next 4 years trying things, than trying to race to success and giving up at the first sign of trouble.
From what I've seen, the people who start successful businesses in their 20s are from generational wealth and can afford the career risk, or their parents are entrepreneurs. Then you also have the people who have been coding since they were 8 years old, so by the time they are 20 already have 10 years of experience. Plus, code has the wild scalability of infinite permissionless leverage (as does social media), so it's possible to make millions from your bedroom as a high school or college student.
In short, give it a shot for a year. If you hate it, you can always go back to a job to learn from your mistakes on someone else's dollar!
