Simplified Deep Learning Deployment
When deploying your computer Vision Transformers & CNN Models, you have to care about not only the model runtime performance but also inference optimization and associated costs. Balancing this trade-off without any impact on user satisfaction is a challenging task. Our platform handles all these automatically for you with zero effort, leveraging AWS, GCP, and OCI for cloud storage and dedicated compute.
Backed and Supported by
Single-click Model Deployment
- Deploy your trained computer Vision Transformers & CNN Models, including classification, detection, and instance segmentation, with a single click.
- Ensure that associated deep learning deployment costs remain low with inference optimization and on-demand compute.
Auto-scaling support
- Traffic prediction is challenging, especially for high-performance GPUs in AI inference.
- In high traffic, more model instances are needed to maintain performance, leveraging NVIDIA GPU acceleration.
- During low traffic, extra instances should be shut down to optimize costs in AWS, GCP, or OCI environments.
- Our platform handles automatic scaling for you, ensuring optimal cloud storage utilization and performance stability.
Dashboard for Real-Time Traffic Visualization
- Real-time traffic analysis is essential when deploying state-of-the-art models for detection and segmentation.
- A deployment dashboard is provided to visualize real-time traffic rates and monitor model drift.
- Adjust deployment settings to optimize performance based on drift monitoring and inference optimization.
Drift Sample Visualization
- Visualize data distribution shifts affecting the latest computer Vision Transformers & CNN Models.
- Helps in collecting more samples using data labeling and annotation tools for retraining.
- Focus on varying distributions over time with data visualization and data versioning.
Dataset Creation & Model Retraining
- Understand reasons for performance degradation in AI inference.
- Easily create new datasets from real application inference samples using ML-Assisted Labeling.
- Follow the pipeline for model retraining and deployment using state-of-the-art models optimized for NVIDIA GPU and AWS, GCP, OCI compute.
API Support for Integration into User APP and Products
We provide the necessary API support to integrate detection, classification, and segmentation models into healthcare, retail, and manufacturing applications.
Our APIs ensure seamless AI inference with OpenVINO, TensorRT, and ONNX optimizations.
Multiple API Key Support for Better Security
We support multiple API keys so that you can easily track your incoming traffic and enable/disable API keys for better security, ensuring secure AI training and AI inference.
Monitor API usage with built-in model monitoring and drift monitoring tools to ensure high reliability in video monitoring and surveillance camera applications.
