Cool, you are building vision models!
But can you deploy 📦 and scale 🚀 them?
You are not alone. At UUG.AI, we scale machine learning models and computer vision algorithms to transform vision into innovative and impactful solutions, enriching lives and industries.
Building a ML model is a start..
Deploying it into the field is the real challenge
Engineering a machine learning model is complex, but deploying and integrating it, is even more so. An apparently simple task involving a few cameras can escalate into a nightmare when scaling to hundreds or thousands of video streams.
Dealing with various streams and camera brands in edge hybrid cloud environments can lead to the unintentional creation of a Video Management System (VMS), wheras you've intended to build a machine learning model at the beginning.
Scaling your camera deployment
MLOps at the center
Your models up to date wherever they are
Once you have a solid plan to deploy and scale your machine learning models, it's evenly important to keep them up to date. The world is changing, and so are your models. Moving your models from development to production is a challenge, and it's even more challenging in a decentralized deployment.
Using MLOps principles, you keep track of your models, and roll out new versions in a controlled way. You can monitor the performance of your video models, and decide to roll back to previous versions if desired. Wherever your models are running, we can keep them up to date at all times.
Build your application on top
An open platform to bring your models alive
The data your models generate is valuable, but in the end, it is just data. To make sense of it, it is brought to life with additional visual components, such as live streaming video playback and many other triggers and functions. Security and data governance with RBAC is required from the start, not after your model is deployed.
You don't want to spend months or years building this visualisation yourself, but should be able to benefit from an open platform, that allows you to build your application on top or integrate with. In the end you are building a machine learning model, not a video management system.
Visualise your model output
