The curse of monolith
What's wrong with products:
Apart from other solutions that provide tools for data labeling and machine learning, Supervisely is built differently. Stay with us for 5 minutes and find why choosing Supervisely will secure your platform for years.
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.
What's wrong with products:
Since 2017 we felt this pain many times and learned from our customers what they want from the platform. That's why we come to understanding that no software can cover every task, from image classification to LiDAR tracking, from labeling to performance analysis, cannot adapt to new challenges fast enough and be customized in a way that every company can have a tailored solution for their very own case.
The idea is to provide a foundation for developing and running applications — just like in OS, like Windows or MacOS. Here how it works:
Now, with App Engine, we are not bound by those limitations anymore. Moreover, here in Supervisely we can focus on core functionality and keep developing the platform, while Supervisely grows with new machine learning tools, model integrations and other applications.
Simplicity of creating a Supervisely Apps has already led to development of hundreds of applications, ready to be run within a single click form your dashboard and get the job done.
Train and run various neural networks, add new models and productivity tools to labeling toolboxes, automate routine tasks — like in a real AppStore, there should be an app for everything.
Let's make a quick recap and summarize the key differences which make Supervisely an ultimate choice for every company, looking for a platform that will support their computer vision development for years to come.
A fully customizable AI infrastructure, deployed on cloud or your servers with everything you love about Supervisely, plus advanced security, control, and support.Start 30 days free trial➔
We use Supervisely since 2019. The key advantage of this tool is that Supervisely provides a complete data treatment pipeline. An important advantage is that a Supervisely instance can be deployed autonomously on a Client infrastructure, and distributed on different servers.
It helps to treat enterprise’s internal and often confidential data in a secured way. Together with a user-friendly interface, a clear documentation and a friendly and reactive support team it helps us to do Data Scientist work better and faster.”
We originally set out to look for tools that could help us with data annotation, and we discovered that Supervisely excels at that and much more. It has become an integral part of our workflow in annotation, model training, and evaluation.
We've been exceedingly impressed with the customer support, addition of new features, and the flexibility of the publicly available SDK/API. The Supervisely team has also been fast to respond to support questions, and has shown a lot of openness when given feedback on potential improvements.
Speak with people who are on the same page with you. An actual data scientist will:
Get accurate training data on scale with expert annotators, ML-assisted tools, dedicated project manager and the leading labeling platform.Order workforce