Efficient Image Classification Serving with ONNX and ONNXRuntime

Deploying machine learning models efficiently for production can be challenging, especially when optimizing for performance and resource usage. This course provides a foundational understanding of how to prepare, optimize, and serve image classification models using ONNX and ONNXRuntime, enabling you to build high-performance, resource-efficient inference pipelines. You'll learn to streamline your deployment process and ensure your models run effectively in various environments. What you'll learn: * Understand the fundamentals of ONNX for universal model interchange. * Learn to convert and optimize deep learning models into the ONNX format. * Apply ONNXRuntime for high-performance and cross-platform model inference. * Configure basic API endpoints to serve image classification predictions. * Practice deploying containerized ONNX models for scalable inference. * Implement best practices for efficient model serving in production environments. The course begins with the core concepts of ONNX and model optimization, progresses through practical conversion steps, and culminates in serving optimized models via ONNXRuntime with basic API integration and containerization. This course is designed for beginners in machine learning and Python programming who want to learn practical model deployment. No prior experience with ONNX, ONNXRuntime, or model serving is required. Begin your journey to deploying fast and efficient image classification models today.