A comprehensive guide to integrating ML models into backend systems using frameworks like TensorFlow Serving, PyTorch, and ONNX Runtime, featuring a practical recommendation engine implementation. The
article covers the entire workflow from model development to production deployment, addressing common challenges and best practices for scalable ML integration.
Reasons to Read -- Learn:
how to implement a complete ML model deployment pipeline, including practical code examples for building a recommendation engine using PyTorch and TorchServe
pros and cons of three major ML serving frameworks (TensorFlow Serving, PyTorch with TorchServe, and ONNX Runtime) and understand which one best fits your specific use case
concrete solutions for common ML deployment challenges, including how to optimize latency through caching, scale using containerization, and implement secure model updates through versioning and CI/CD pipelines
publisher: @priyanshu011109
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