Month: 2025-01 Key feature delivered: Quantization-Aware Training feature fake_quant_with_min_max_vars added to keras-team/keras. This work ports TensorFlow's fake_quant_with_min_max functions to Keras, enabling training-time quantization simulation by defining clamping ranges and bit widths. The implementation supports both per-tensor and per-channel quantization and is backed by comprehensive tests ensuring correctness across backends and configurations. Major bugs fixed: No explicit bug fixes documented for this month. Primary focus and value came from feature delivery and expanded test coverage to reduce future regressions in quantization behavior. Overall impact and accomplishments: Enables developers to train quantized models with accurate simulation of quantization effects, improving deployment readiness on resource-constrained devices and easing migration for TF users to Keras. The feature reduces quantization risk by validating correctness across backends and configurations, contributing to more robust quantized models and streamlined model optimization workflows. Technologies/skills demonstrated: Quantization algorithms, per-tensor and per-channel quantization, API porting from TensorFlow, cross-backend test coverage, test-driven development, and code review readiness for deployment on edge devices. Business value: Shorter path to efficient, deployable quantized models; improved model size and inference efficiency without sacrificing training fidelity; stronger alignment with TensorFlow quantization APIs for ecosystem interoperability.