Serving ONNX Models for Image Classification

Struggling to move your trained image classification models from development to production? Efficiently serving machine learning models is crucial for real-world applications. This course guides you through the process of converting trained models to the ONNX format and deploying them for inference. By the end, you'll be able to set up robust and performant serving pipelines for your image classification tasks, making your AI solutions accessible and scalable. What you'll learn: * Understand the role and benefits of the ONNX format for model interoperability and deployment. * Convert popular image classification models from frameworks like PyTorch or TensorFlow to ONNX. * Develop Python scripts for efficient ONNX model inference and prediction. * Containerize your ONNX serving application using Docker for consistent and portable deployment. * Build a basic REST API to expose your ONNX model for real-time predictions. * Apply best practices for optimizing ONNX model inference performance. The course begins with an introduction to ONNX and its advantages, then walks through model conversion. Subsequent sections cover building inference pipelines, containerizing applications, and deploying models via a simple web API. This course is designed for beginners in machine learning and MLOps who want to learn practical model deployment. No prior experience with ONNX or model serving is required. Start building and deploying your own image classification inference services today.