Start with the deployment constraint
Quantization represents values with reduced numerical precision. It can reduce model size and may improve performance on compatible hardware, but the benefit depends on supported operators and runtime behavior. Measure the original model on the intended device before choosing an optimization target.
A model is 100 MB but meets latency requirements. State whether storage, latency, or quality is the primary optimization goal.
Choose representative calibration inputs
Some integer quantization approaches need representative inputs to estimate activation ranges. Use realistic preprocessing and cover expected operating conditions. A tiny calibration set of unusually clean samples can miss the ranges seen in practice. Calibration data is not a substitute for an independent quality evaluation set.
List capture conditions you would include when calibrating a camera model for indoor and outdoor use.
Measure quality and device performance
Compare the converted model with the original on identical held-out cases. Record aggregate quality, critical slices, latency distribution, memory, and startup time. An unsupported operator may fall back to another execution path, erasing the expected speedup. A smaller file alone does not establish a successful optimization.
The file shrinks by 60% but device latency increases. Identify two runtime properties to investigate.
Release against explicit gates
Set acceptable quality degradation and device limits before examining conversion results. Keep the original artifact and conversion configuration for rollback. If a critical slice fails, investigate representative data, supported operators, or a training-aware approach rather than accepting the average result.
Write a release rule requiring latency under 80 ms and no more than one percentage point of recall loss on a critical slice.
Does a smaller converted file prove faster inference?
Keep exploring
Use the official certification page and its linked resources to validate your coverage. For guided labs, explore the Google Skills learning path. Revisit the practical exercise and explain your decision in your own words before moving on.