11–20 of 78 posts
So if you say you're going the ML route your perceived value is much higher.
Time to setup up scikit learn and tensorflow algorithms to make predictions: 4 hours. Time to setup python scripts which could parse through the excel spreadsheets, figure out which row corresponded to gross profit margin, and a few other "standard" metrics: ??? unknown because I gave up at about 80 hours trying to figure out rules to process all the different spreadsheets and how names were determined.
I had a professor who was doing machine learning + chemistry. He was building up his own personal database for machine learning. He spent ~5 years with about 500 computers building the database so that he would be able to do the actual the machine learning.
There are surely some startups for which this is bullshit. But the good version of it is: - take some valuable task that's never been successfully automated before - do it manually (and expensively) for a while to acquire data - build an automated system with some combination of regular software and ML models trained on the data - now you can do a valuable task for free - scale up and profit The risk is that it's har…
I think you forgot most important option. It may not be a data problem. You may have all the data in the world and still not being able to solve the issue.
Capabilities of ML are much more limited than hype makes people believe
Because there is a real moat with data ownership and pipelines. If you want to do any analysis you quickly find that learning to properly use scikit-learn and tensorflow (or your machine learning algorithm of your choice) is atleast an order of magnitude lower of work than getting the data. For instance, I wanted to build a machine learning algorithm which took simple data from the SEC filed 10-Q and 10-K, which are…
There are surely some startups for which this is bullshit. But the good version of it is: - take some valuable task that's never been successfully automated before - do it manually (and expensively) for a while to acquire data - build an automated system with some combination of regular software and ML models trained on the data - now you can do a valuable task for free - scale up and profit The risk is that it's har…
This seems too easy a recipe to be worth it in the medium term - there is no moat. Better cover your data with very strict laws, like google does with its exclusive deals for medical data use.
The laws were already quite strict (HIPAA) and the data relatively inaccessible.
Google jumped through all the hoops to get it. And an exclusive contract never hurts no matter what the service. (Logistics, payment processor, etc.)
It's like the gold rush except the gold (data) is easy to make, and very very cheap. hmm better sell shovels.
(Of course, there is the ever more popular route of making services for enterprises wanting to outsource basic stuff.)
Earlier quoted context omitted.
This seems too easy a recipe to be worth it in the medium term - there is no moat. Better cover your data with very strict laws, like google does with its exclusive deals for medical data use.
Google didn't add any "laws" around that data though. The laws were already quite strict (HIPAA) and the data relatively inaccessible. Google jumped through all the hoops to get it. And an exclusive contract never hurts no matter what the service. (Logistics, payment processor, etc.)
Thats why this is unfair. The health industry incentivizes (and often mandates) open publishing of scientific results, but patient data is reserved with gold chains for the exclusive use of google