The clear use case is serverless—without the complications of DynamoDB (expensive, 0.03/GB read), DynamoDB+DAX (VPC complications), or Redis (again, VPC requirements).
This instantly makes a number of applications able to run directly on S3, sans any caching system.
It’s very clear that Copilot/GPT-4 give superpowers to the generalist—-rapid prototyping—-and to the ultra-specialist—-no more yak-shaving in other domains.
Most roles that are run-of-the-mill “knows framework X” just enough to support naive clients or actually high/value teams, will indeed go to zero.
Which is amusing because 90% of the “AI” companies being started are run by people with no actual expertise, who probably are the most replaceable parts of deploying actual high/end systems.
Hiring people with low fluid intelligence (-2 stdev change with age) and people with low intelligence (sorry bootcampers, you would already have a quantitative degree if you had a high qualitative IQ)—-what a solution
At its peak, Coinbase stock had appreciated less since its seed round than simply holding Bitcoin
Thanks to brilliant management, like handing out a billion dollars to some google exec who knew nothing about crypto and shipped a product no one uses, Coinbase is now indexed to the average price of the shitcoins it consistently lists.
Therapy and medicine won't help. Usually the answers are: clean up the negative people around you (fire that person as your boss, network and take tons of other interviews sufficient to move or have a fallback option so you worry less), and branch out on your actual life (travel more, do something new, etc.)
You could also accept that Faangs are full of slackers and coasters--stop tying your self-esteem to your work or your boss (who is actually your adversary, squeezing as much work from you for the lowest price). Do enough to be of interest to other teams there, have a few references and savings and just assume you will be let go, and focus on improving your life and your next opportunity.
This largely misses the mark. Kaggle is a machine learning competition platform for a small set of exceptional machine learning talent, and a lot of students or hangers-on.
"Machine learning projects – if ML is being attempted at all – are in early stages, using traditional methods that are best-suited for high-RAM CPU rather than GPU SKUs (ex: scikit-learn and clustering approaches)."
The idea that the machine learning being done there is in "early stages" is laughable, given the prize pools and sheer competitiveness usually move well past existing SOTA--usually moving forward benchmarks on Google's image classification and labeling (!) benchmarks and other areas where enormous teams can't match the top few.
Part of what you're seeing is the mass of survey participants, who show up to fork notebooks and fake ML skills, are the people who haven't exactly established themselves in the field (https://commons.m.wikimedia.org/wiki/File:Survivorship-bias....), while the top-end is too small a group to fully characterize with low-powered clustering.
Even five years ago, most real-world MLEs knew how to use AWS, were deploying actual machine learning models (granted, more primitive than today's methods), and I'm wouldn't be so glib about calling anything "early stage" even then given the raw business value it provides.
She outright spits on the way Bezos made his money, when he has one of the most honest and efficiency-enhancing paths to earning his fortune.
Is the world a better place because Gates used blatantly illegal monopolistic practices to stifle innovation in the OS space? Or because Facebook is willing to engage in dirtier data-sharing, growth, and addictive product design?
Whatever one thinks of those, Amazon created a market for the long tail of books, media, and then goods that simply didn't exist. And then followed it up with one of the most important platforms in the history of the tech and startup ecosystem.
He's done more good with those than she'll ever do by giving his earnings to whatever is most fashionable at the moment.
The author understands little to nothing about programming. I'd bet on the law of the straight line over his/her uninformed take.
Python is slow, but PyTorch is fast, GBTs are fast, Cython is fast, Pandas and Numpy are fast (and even faster libraries or even basic joblib code can parallelize these).
Anything that needs to be fast either is or can be made fast--and most compute in data-intensive applications exists inside these optimized libraries anyway.
This instantly makes a number of applications able to run directly on S3, sans any caching system.