Applied AI Systems
Applied AI systems, LLM architecture, copilots, retrieval, and document intelligence.
Resume
Resume overview
Yangming Li works at the intersection of applied AI, AI system architecture, data science, product systems, and engineering execution. This page summarizes the focus areas already represented across the site.
Technical Focus
Core focus areas
Machine Learning & NLP
Statistical machine learning, NLP, topic modeling, and trustworthy ML.
Data Products
Data engineering, data products, MLOps, and experiment infrastructure.
Product Systems
Product strategy, analytics systems, decision-support tooling, and production delivery.
Evidence
Credentials, learning, and selected writing
Credentials and Learning
The site references CFA and FRM credentials and includes a courses and certificates page with supporting learning records.
Selected Writing
For evidence of technical focus, review the blog index, project themes, and articles on MLOps, MCP, and A/B testing systems.
Selected technical writing
Evidence for the resume themes
A small set of public notes that support the applied AI, LLM evaluation, data product, and experimentation themes summarized above.
AI Agent Evaluation Is More Important Than Prompting
Why production AI agents need custom eval sets, trajectory checks, calibrated judges, regression tests, and business-ready metrics.
Read articleModel Context Protocol Guide
Tool and context integration patterns for connected AI systems.
Read articleDatabricks Lakehouse Guide
Delta Lake, MLflow, Unity Catalog, and data platform implementation notes.
Read articleA/B Test Engineering Guide
Feature gates, experiments, reliable launch measurement, and experimentation infrastructure.
Read article