What's wrong with most of AI software?
The paramount factor when choosing a labeling software
At some point every computer vision team comes to understanding that it's too hard, slow and immature to manage growing datasets manually, use basic open-source labeling tools and spend too much time on integration between multiple solutions since there is no standardization. It's time to choose a platform.
Now, many companies choose creating an in-house solution instead of switching to an enterprise software. Why? Well, it all runs down to a simple wish:
A company wants to be sure that their current and future tasks are solvable.
The main issue with most solutions on the market is that they build as products. It's a black box developing by some company you don't really have impact on. As soon as your requirements go beyond basic features offered and you want to customize your experience, add something that is not in line with the software owner development plans or won't benefit other customers, you're out of luck.
The curse of monolith
What's wrong with products:
😞 Too many feature requests to implement at once within a single development team
😞 Software gets overcomplicated with hard to use and understand tools
😞 As functionality grows, it becomes hard to develop and maintenance product software
😞 Community stays overboard with no ways to improve software with things they need
😞 You as a customer should rely on product owner, you cannot do any changes yourself
😞 Only general problems that fit most customer are addressed, good luck if you are not one of them
