What topics does this blog cover?

This site covers applied AI systems, LLM workflows, data products, and experiment infrastructure with practical writing across AI/ML, product, and engineering.

Yangming Li is an AI Engineer and Product Builder based in Vancouver, focused on LLM systems, data products, and experiment infrastructure for healthcare, finance, and enterprise teams.

How can I contact Yangming Li?

You can contact Yangming Li via email at liym1@hotmail.com, through LinkedIn, or by using the contact form on the website.

Yangming Li | Applied AI Systems for Real-World Workflows

Runtime Snapshot

class YangmingLi: @staticmethod def builds() -> list: return ["LLM systems", "Data products", "Experiment infrastructure"] @staticmethod def serves() -> list: return ["Healthcare teams", "Finance teams", "Enterprise teams"] @staticmethod def outcomes() -> list: return ["Shipped workflows", "Faster decision loops", "Reusable internal tooling"] # Instantiate Yangming Li builder = YangmingLi() print(f"Builds: {builder.builds()}") print(f"Serves: {builder.serves()}") print(f"Outcomes: {builder.outcomes()}")

>focus_areas = 3

>industries_covered = 5

>delivery_mode = "prototype to production"

Favorite Quote

My Favorite Quote

Tool Builder Mindset

"I'm a tool builder. That's how I think of myself. I want to build really good tools that I know in my gut and my heart will be valuable. And then, whatever happens, is... you can't really predict exactly what will happen, but you can feel the direction that we're going. And that's about as close as you can get. Then you just stand back and get out of the way, and these things take on a life of their own."

Learning Loop

Courses Learned

View All Certificates

0 courses+

I've completed over 15 courses in programming, machine learning, MLOps, and data science from platforms like DataCamp and Coursera. Click here to view all certificates.

Python Programming Deep Learning SQL MLOps Docker Git & GitHub

Selected Writing

Browse writing by topic. AI/ML, product, and engineering now live under one blog view.

【2026】 pytorch review01

[ML] A practical PyTorch review of tensors, shapes, broadcasting, reshaping, and torch.distributions.
【2025】[MLOP] Leveraging Docker in Machine Learning and Data Science

[MLOP] A comprehensive guide to using Docker for ML/DS projects - from development to deployment
【2025】[MLOP] A Must-Have Skill for Efficient Model Management and Deployment

[MLOP] Understanding MLOP principles and implementation with MLflow and Weights & Biases
【2025】[ML]Trustworthy machine learning.

[ML] What is trustworthy machine learning.
【2025】[ML]Fine Tune Large Language Model for sentiment analysis

[ML] sft Large Language Model
【2025】[ML]Fine Tune Large Language Model for sentiment analysis2

[ML] sft Tune Large Language Model2
【2025】[MLOP] Ray.

[MLOP]What is ray.
【2025】[ML]Why Have Decoder-Only Architectures Become the Standard in LLMs?

[ML] Understanding the dominance of decoder-only architectures in modern LLMs
【2025】[ML] Building an Enterprise-Level AI Agent for Document Transformation

[ML] A comprehensive guide to building document processing AI agents using LlamaReport and LlamaCloud
【2025】[ML] The Model Context Protocol (MCP): Building a Connected Future for AI

[ML] A comprehensive guide to understanding and implementing the Model Context Protocol for AI integration
【2025】[ML] Uncertainty Quantification for LLMs: Teaching with UQLM

[ML] A hands-on teaching guide for using UQLM to quantify and understand uncertainty in large language models.
【2025】[ML] Build Autonomous AI Workflows with n8n: A Technical Introduction to Agentic Automation

[ML] A comprehensive guide to building agentic automation systems using n8n's AI Agent nodes.
【2025】[Classical ML] Exploring Random Forest: A Powerful Ensemble Learning Algorithm

[Classical ML] Dive into the mechanics, advantages, and applications of Random Forest - an ensemble learning algorithm that combines multiple decision trees for robust classification and regression tasks.
【2025】[ML] Machine Unlearning: A Complete Technical Guide

[ML] A step-by-step guide to implementing machine unlearning systems with algorithms, APIs, and monitoring processes.

Yangming's Product Blog

Building a Product That Scales into a Company: Lessons from the 4U Framework

A comprehensive guide to scaling products into successful companies using the 4U Framework and other strategic approaches.
The Essence of a Successful Product: Insights for Product Managers

A deep dive into what truly makes a product successful from a product manager's perspective.
A Deep Dive into Jira: Agile Project Management for the Modern Team

A comprehensive guide to using Jira for agile project management and team collaboration.

Yangming's Engineering Blog

[Statistics] Choosing the Right Statistical Test for Survey Analysis

[BI] A comprehensive guide to statistical tests for analyzing survey data
[Data Engineering] Understanding Databricks: A Comprehensive Guide with Real-World Examples

[Data Engineering] A deep dive into Databricks features and implementation with practical examples
[Engineering] Understanding Kubernetes: A Comprehensive Guide

[Engineering] A deep dive into Kubernetes architecture, components, and practical implementation
[Engineering] Introduction to Polars: A Fast and Efficient Data Processing Library

A comprehensive guide to using Polars for high-performance data processing in Python
[Machine Learning] Deep Neural Networks (DNN) Explained: Core Principles and Implementation

A comprehensive guide to understanding deep neural networks (DNNs), including forward and backward propagation, optimization algorithms, and PyTorch implementation
[Engineering] Deep Learning Engineering with JAX: A Modern Approach

A comprehensive guide to using JAX for high-performance machine learning and numerical computing
[Engineering] Feature Flags as Experiment Infrastructure: A Practical Guide to A/B Testing at Scale

[Engineering] A deep dive into productionizing A/B testing—from 5 experiments/year to 50,000/year—covering CUPED variance reduction, sequential testing, Bayesian vs. frequentist methods, plus a complete SOP template and resource checklist.
[Engineering] From Zero to One: Building and Publishing a Python Package

[Engineering] A comprehensive guide to creating, testing, documenting, and publishing Python packages following modern best practices
[Statistics] Generalized Linear Models (GLM): A Comprehensive Overview

[Statistics] A deep dive into GLMs, their applications, and implementation in both statistical and machine learning contexts

Yangming's Self-Learning

Carnegie Mellon University Advanced NLP Course Notes

These are my study notes from CMU's Advanced Natural Language Processing course. The notes cover fundamental concepts and advanced topics in NLP.
MIT Data Structure and Algorithms Course Notes

These are my study notes from MIT's Data Structure and Algorithms course. The notes cover fundamental algorithms, data structures, and their practical implementations.
MIT Principles of Computer Systems (6.826) Course Notes

These are my study notes from MIT's Principles of Computer Systems course. The notes cover distributed systems, concurrency, fault tolerance, and system design principles.
MIT Computation Structures (6.004) Course Notes

These are my study notes from MIT's Computation Structures course. The notes cover digital systems design, Boolean logic, computer architecture, and assembly language programming.

Selected Work

Representative work themes across healthcare, finance, and enterprise teams, centered on systems that improve real workflows instead of staying as demos.

LLM Systems

Applied AI for document and knowledge workflows

Designing LLM-assisted systems for document transformation, retrieval, review, and operational handoff in enterprise environments.

LlamaReport guide MCP guide n8n workflows

Data Products

Decision-support products for complex teams

Building analytics and product experiences that help healthcare, finance, and public-sector teams move from raw data to better operational decisions.

Product strategy Product fundamentals Workflow systems

Experiment Infrastructure

From experimentation to production delivery

Shaping the foundations for reproducible ML, faster iteration, and experiment-ready delivery with MLOps and engineering discipline.

A/B testing systems MLOps foundations Data platform guide

Yangming's Thoughts

Why "Taste" Matters in Science — and in Technology

Exploring Nobel laureate Yang Zhenning's concept of 'taste' in research and how it applies to technology and product development. What separates the merely competent from the truly visionary in science and tech.
Interesting Resource: Calculating Empires

I recently discovered an fascinating interactive resource called "Calculating Empires: A Genealogy of Technology and Power Since 1500". This comprehensive visualization maps out the intricate relationships between technology, power, and human history over the past 500 years.
Knowledge Flow

An interactive platform for visualizing and exploring connected knowledge across various domains. Knowledge Flow helps discover relationships between concepts and ideas in a structured format.