March 2025 monthly summary for the apache/singa repository. Delivered a Cardiovascular Disease Prediction Model (MLP) using the SINGA framework, introducing an end-to-end ML feature with linear layers, ReLU activations, softmax cross-entropy loss, and training methods that support multiple distribution options, plus a reusable model instantiation utility. Commit 3509235f73fbd92b314e090ff98fb967190cc4c8 recorded as the implementation. Major bugs fixed: None reported for this feature in March 2025; no blockers encountered. Impact: Enables scalable cardiovascular risk prediction within Singa, accelerating experimentation and deployment of ML-based healthcare analytics. Improves reusability and maintainability with a modular design and clear commit traceability. Technologies/skills demonstrated: ML model development, SINGA-based architecture, modular training utilities, and strong commit hygiene and documentation.

December 2024 monthly summary: Focused on improving onboarding and usability for TED_CT_Detection within the apache/singa repository. Delivered a README usability improvement that clarifies the training execution steps and includes a detailed command example to help new users run the training script. This work reduces setup friction, improves reproducibility, and accelerates onboarding for users and contributors. No major bug fixes were reported within the provided scope.