Introduction to Model Deployment
Explore the key concepts of deploying AI models across diverse hardware environments. Understand how to optimize and convert trained models for mobile devices, embedded systems, and servers using frameworks like TensorRT, TensorLite, PyTorch Mobile, ONNX, and OpenVINO. This lesson prepares you to implement efficient inference in real-world deployment scenarios.
Deployment of an AI model
It’s time to learn the deployment stage and the real environment. We will use our trained model to ask for its predictions using its ready-to-use weights. This is also called inference time.
Usually, a deployment environment has different hardware features than training. We can have a GPU in our local machine to train our models, but we might want to ...