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Leveraging Machine Learning for Backend Applications
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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