High-Performance Compute, Optimized for Vision Workloads
Train, deploy, and monitor vision models on the most powerful GPUs without managing infrastructure or wasting spend.
Train and Deploy the Models That Work Best for You
Integrate models not yet available in our platform while leveraging Matrice's full stack for training, inference optimization, and deployment. Upload your own PyTorch, TensorFlow, or ONNX model with full lifecycle support: train, fine-tune, deploy. Compatible with NVIDIA GPU, AWS, GCP, and OCI. Deploy to cloud or edge environments. Access all Matrice MLOps features with custom models.
Fine-Tune with Flexible Hyperparameter Control
Expose and experiment with your model's hyperparameters to optimize performance for any use case. Define and adjust key training parameters. Integrate custom search spaces for tuning. Use Auto-ML with your own model logic. Optimize for inference latency or accuracy. Ideal for production tuning and research iterations.
Seamless Model Integration Without Heavy Refactoring
No need to rewrite your pipeline—just a few lines of code to make your model fully deployable with Matrice. Add a simple action handler with ActionID. Automatically receive parameters for inference. Monitor training and inference progress. Works with cloud-based and private data workflows. Avoid vendor lock-in with portable model logic.
Deploy with Copyright and Licensing Compliance
Retain control of your intellectual property while ensuring compliance with commercial deployment restrictions. Upload proprietary or open-source models. Ensure licensing compliance for commercial use. Matrice does not redistribute or reuse your model. Compatible with ONNX, TensorRT, OpenVINO. Suitable for regulated industries.
Transforming Pixels
into Intelligence
Real time intelligence for existing cameras; One platform. Many Applications. Any Scale