Model optimization
2 stories filed under Model optimization on Beyond Market Intelligence. The newest of them: “Five Production Methods to Make Your LLM Leaner and Faster” and “Balancing the Unbalanced: Smarter Loss Functions for Medical AI”. Every parameter in your model costs money, and too many of them mean your inference bill climbs with every prompt. Three models collapsing toward BIRADS 1 is a familiar wall when a dataset leans that hard. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Model optimization story on Beyond Market Intelligence, newest first.
Five Production Methods to Make Your LLM Leaner and Faster
Every parameter in your model costs money, and too many of them mean your inference bill climbs with every prompt. Quantization and pruning are how you cut that weight without gutting performance, and skipping them leaves you paying for latency you do not need. Quantization and pruning cut model weight without gutting performance, and five production methods demonstrate how. If you are still unpacking how distributed systems shape model training, our guide to distributed algorithms pairs well with this one.
Balancing the Unbalanced: Smarter Loss Functions for Medical AI
Three models collapsing toward BIRADS 1 is a familiar wall when a dataset leans that hard. The VinDr imbalance is likely steering your cross-entropy, even with class weights and center loss in the mix. You are not wrong to question the loss function, but the weights may need recalibration or a focal-style adjustment to resist the majority pull. Before abandoning the approach, audit your sampling strategy and weight initialization.