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Optimizing Quantization Workflows for Efficient ML Deployment

Many ML deployment discussions prioritize model quality over infrastructure, leading to performance issues and high costs. Quantization can help, but its implementation is often plagued by pain points such as accuracy collapse, tooling fragmentation, and hardware-specific behavior.

Computing & Technology, Computer Science, Machine Learning