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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #142
You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You…
The correct answer is B. Dynamic range quantization. The requirement is "Without training a new model" hence dynamic range quantization.
Submitted by yuriko_h· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions
Question
You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn't meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?
Options
- AWeight pruning
- BDynamic range quantization
- CModel distillation
- DDimensionality reduction
How the community answered
(40 responses)- A3% (1)
- B80% (32)
- C13% (5)
- D5% (2)
Explanation
The requirement is "Without training a new model" hence dynamic range quantization.
Topics
#Model optimization#Quantization#Inference latency#Mobile ML
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