Balancing model accuracy with computational cost is a critical aspect of deploying AI solutions, often involving trade-offs between precision and resource efficiency. Engineers typically optimize models by tuning hyperparameters, selecting appropriate architectures, and employing techniques like model pruning or quantization to reduce computational demands while maintaining acceptable accuracy levels.

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