Optimize Your Computer Vision Transformers & CNN Models for Different Environments
Our platform supports all the runtime optimizations that can help reduce your model prediction latency and increase model throughput significantly. By leveraging inference optimization, hyperparameter tuning, and dedicated compute for AI inference, you can enhance the performance of detection, classification, and instance segmentation tasks across industries such as healthcare, automotive, retail, and manufacturing. Our deep learning deployment service ensures lower latency and reduced long-term deployment costs while supporting cloud storage on AWS, GCP, and OCI.
Backed and Supported by
ONNX Runtime Support
- ONNX is an intermediary machine learning framework for runtime optimization and conversion between different frameworks.
- Converting a model to ONNX format generally boosts performance by around 2x, making it ideal for deploying the latest computer Vision Transformers & CNN Models.
- ONNX is the recommended format for downloading and sharing trained models, ensuring compatibility across AI inference platforms.
- Our platform supports converting any model you train to ONNX format for runtime performance optimization, inference optimization, and cloud-based deployment on AWS compute.
Edge Device Optimization for Android/iOS Devices
- Models trained in PyTorch or TensorFlow cannot be directly deployed to mobile apps without proper inference optimization.
- To run models on mobile hardware efficiently, they must be optimized and converted using frameworks such as OpenVINO, TensorRT, and ONNX for better model selection and deployment.
- Our platform supports converting your trained model to the required mobile device runtime framework while optimizing it for NVIDIA GPU and high-performance GPUs used in on-premise inference.
Online Model Testing with Data Visualization for Optimized Models
- It’s always a good idea to manually test your optimized model before deployment, especially in critical use cases like security camera monitoring, activity recognition, and face recognition.
- Our platform allows you to test your optimized model online and visualize the predictions with real-time data visualization tools before deploying it for video monitoring, object tracking, or pose estimation.
OpenVINO Runtime Support
Leverage Intel hardware by optimizing models with OpenVINO for 2x runtime improvement with float32 and up to 10x with int8 quantization, facilitating efficient inference optimization for security camera applications, surveillance camera processing, and healthcare imaging.
TensorRT Runtime Support
Optimize deep learning models with TensorRT for faster inference and enhanced performance on NVIDIA GPUs, ideal for automotive, manufacturing, and AI training workloads.
Mode-Based Quantization Support
Our platform supports quantization during model conversion, improving inference performance, reducing memory usage, and ensuring efficient parameter fitting for state-of-the-art models used in detection and segmentation tasks.
Model Download Support for Self Deployment
There is no point in training and optimizing your model if you can’t deploy it to your required edge device or private data environment.
Our platform allows model download for deployment on your own cloud storage, on-premise inference system, or Bring Your Own Model (BYOM) infrastructure.
