Start training your model by simply copying the link of the data set. Supports a wide range of computer vision models like detection and classification. Easy data ingestion for training, automatically detects default configuration like the model family and name.

Advanced users can customize settings and train hyper parameters to train the model efficiently for particular data sets, supports multiple Edge hardware.

Easily monitor training performance through visual charts, and detailed reports for accuracy improvements over time and number of Epochs. Feedback loops for new data-set and model versioning.

Automatically optimize inference performance through quantization and graph compilers to instantly improve memory, latency and throughput of your models.

With the accuracy level bar set at -1%, improve model performance without accuracy trade-offs to easily deploy on actual hardware. ENAP takes care of the heavy lifting at the backend and re-trains the model if accuracy levels drop below the threshold.

ENAP optimizer automatically tunes the model to reduce any redundancy, to reduce the computational heaviness from the model, and makes it optimal to be deployed seamlessly on resource-constrained hardware.

Simplify deep learning model inference across multiple frameworks and hardware. Easily deploy to any hardware using our inference engine and save hours of engineering time and effort.

Scale your inference workloads, build, port and optimize computer vision models on any Edge AI platform including Nvidia, Qualcomm, etc.