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Model Quantization: MLOps
Automated benchmarking and quantization workflows across SDK/hardware combos with CI/CD, versioning discipline, and performance analytics.
Model Quantization: MLOps
Automated benchmarking and quantization workflows across SDK/hardware combos with CI/CD, versioning discipline, and performance analytics.
01
Context
The client needed to validate AI processor capabilities and quantify performance gains across model and SDK iterations, with robust CI/CD and reporting.
02
What We Engineered
MLOps pipelines to run benchmarks across multiple SDK versions and hardware combinations.
Power BI dashboard for comparative analytics; versioning/branching process aligned to CI/CD.
Customized quantization parameters validated against model metrics for target hardware.
03
Intelligence Applied
Hardware‑aware quantization using QNN, AIMET; experiment tracking via MLflow; automation with Jenkins.
04
Impact Delivered
Predictable, repeatable model performance evaluation across heterogeneous stacks.
Clear governance over versions and measurable improvements per iteration.
05
Highlights / Stack
QNN, AIMET; MLflow; Jenkins; Power BI.