AI Metadata Team

Built Value Model Insight Platform: Led the development of the Value Model Insight Platform within AIM Graph, achieving 81% RaaS configuration coverage and supporting 88% of value models in system cards. Enabled EU Digital Services Act (DSA) compliance by automating metadata ingestion and lineage. Directed platform design and ingestion automation. Collaborated with staff engineers and partner teams to ensure quality.

Integrated Model Access Control with AIM Metadata Graph: Registered mappings between Model Assets and Access Control Lists (ACL) in the AI Metadata Graph, automating relationships for high-security-rated AI assets. Improved lifecycle management for ACL model assets, ensuring proper handling during deprecation or deletion, and enabled consistent access operations for 245 deprecated model groups.

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Online training Team

Enabled Online Training with Hive dataset: Co-led a multi-phase project with engineers from Data Pre-Processing and Ads Training to prepare Online Hive Training (OHT) for production. Delivered a 66% improvement in data-to-serving latency and enhanced ads performance scores by +0.17%, pending final customer verification.

Identified production models with Prod-intent tagging: Designed and implemented a system to add ”production intent” for recurring production jobs, achieving 95% coverage. Enabled identification of critical production models for SEV criteria, improving reliability and response accuracy.

Reduced Noisy Alerts: Reduced over 88% of daily online training alert noise for the Ads organization by leading cross-functional collaboration to align alert expectations. Achieved an additional 53% noise reduction for the Modern Recommendation Systems organization within the next half.

Launched forecasting Deep Learning model in three marketplaces: Extended the MQ-CNN retail demand forecasting model to Mexico, Brazil and India by collaborating with data scientists on feature selection and training period optimization. Launched the model in India, Amazon’s second-largest market, extending coverage to multiple merchants.

Enable multi-merchant support for forecasting model: Managed a cross-functional team of 5 engineers and coordinated with 5 teams to extend the MQ-CNN model for multi-merchant support in India, Amazon’s second-largest market.

Built ML Explanation tool for Deep Learning forecasting: Built an ML Explanation tool using SHAP (SHapley Additive exPlanations) to analyze feature impact on prediction differences, enabling deep dives into high-severity forecast changes. Designed a serverless architecture for on-demand explanation requests, supporting 87% of high-severity forecast analysis tickets over two years. Expanded the tool to support broader organizational forecasting models, improving accessibility and utility.

Designed automated training and backtesting pipeline: Designed building dynamic tasks in the workflow and API between user, backtesting pipeline, and training pipeline. Implemented model training validation part and metadata management part for traceability.

Built infrastructure for pipeline consolidation: Implemented infrastructure-as-code for consolidating pipelines supporting similar forecasting models, streamlining operations across the organization. Designed the infrastructure using AWS EMR for data processing, AWS Batch for predictions, and AWS Step Functions for pipeline orchestration, improving efficiency and scalability.

Automated Forecasting Rampdown: Built a rampdown application to align traditional forecast model outputs with prior-year trends, improving Q4 peak week accuracy by 4.3% for traditional models and 0.7% for deep learning models.