google-ai-edge/ai-edge-quantizer
Jun 2026 – Jun 2026
1 Month active
Languages Used
Python
Technical Skills
Machine LearningPythonPython DevelopmentQuantizationdocumentationmachine learning
Mai Huynh
PROFILE
Mai Huynh
Worked on enhancing the google-ai-edge/ai-edge-quantizer repository by expanding the MNIST example to support multiple quantization methods, including programmatic recipes, advanced post-training quantization, static range quantization, blockwise quantization, Hadamard quantization, and integration with external JSON recipes. Leveraged Python and machine learning expertise to implement these quantization techniques, enabling more efficient deployment of edge ML models on constrained devices. Updated and streamlined documentation to improve developer onboarding and clarify usage, ensuring traceability through detailed commit history. This work broadened deployment options, reduced integration risk, and accelerated time-to-value for users building and deploying quantized models with AEQ.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
563
Activity Months1
Your Network
4973 peopleSame Organization
@google.com4953
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Work History
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 — AI Edge Quantizer (AEQ) enhancements for MNIST: added multiple quantization methods (programmatic recipes, advanced post-training quantization, static range quantization, blockwise quantization, Hadamard quantization, external JSON recipes) and refreshed usage docs to streamline edge deployments. No major defects reported; this work reduces integration risk and accelerates time-to-value for edge ML models. Impact: expanded deployment options, smaller/faster models on constrained devices, improved developer onboarding. Skills demonstrated: edge ML quantization techniques, Python/API usage, documentation discipline, Git traceability.
2 Commits • 1 Features
Jun 1, 2026June 2026 — AI Edge Quantizer (AEQ) enhancements for MNIST: added multiple quantization methods (programmatic recipes, advanced post-training quantization, static range quantization, blockwise quantization, Hadamard quantization, external JSON recipes) and refreshed usage docs to streamline edge deployments. No major defects reported; this work reduces integration risk and accelerates time-to-value for edge ML models. Impact: expanded deployment options, smaller/faster models on constrained devices, improved developer onboarding. Skills demonstrated: edge ML quantization techniques, Python/API usage, documentation discipline, Git traceability.
June 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Machine LearningPythonPython DevelopmentQuantizationdocumentationmachine learningquantization
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