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[MLOP] Leveraging Docker in Machine Learning and Data Science
[MLOP] A comprehensive guide to using Docker for ML/DS projects - from development to deployment
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[MLOP] A Must-Have Skill for Efficient Model Management and Deployment
[MLOP] Understanding MLOP principles and implementation with MLflow and Weights & Biases
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[ML]Trustworthy machine learning.
[ML] What is trustworthy machine learning.
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[ML]Fine Tune Large Language Model for sentiment analysis
[ML] sft Large Language Model
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[ML]Fine Tune Large Language Model for sentiment analysis2
[ML] sft Tune Large Language Model2
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[MLOP] Ray.
[MLOP]What is ray.
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[ML]Why Have Decoder-Only Architectures Become the Standard in LLMs?
[ML] Understanding the dominance of decoder-only architectures in modern LLMs
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[ML] Building an Enterprise-Level AI Agent for Document Transformation
[ML] A comprehensive guide to building document processing AI agents using LlamaReport and LlamaCloud
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[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
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[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.
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[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.
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[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.
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[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 Li · AI/ML · 产品 · 工程博客
I am a Data & Product Enthusiast.
I am interested in AI/ML & product design & information visualization.
Welcome to my tech blog! My name is Yangming Li, and I'm currently based in Vancouver, working as a Data Product Scientist. Here, I'll share practical techniques and engineering insights for building real-world data products, drawing on years of hands-on experience in AI/ML and product management across the finance, healthcare, transportation, environment, and education sectors. Whether you're a beginner or a seasoned professional, you'll find reusable methods and inspiration through case studies, tool selection, deployment workflows, and code examples.













About Me
class YangmingLi:
@staticmethod
def roles() -> list:
return [
"Product Manager",
"AI Engineer",
"Machine Learning Engineer",
"Data Scientist"
]
@staticmethod
def industry_experience() -> dict:
return {
"Finance": ["BCIMC", "Industrial and Commercial Bank of China"],
"Healthcare": ["Provincial Health Authority", "Island Health"],
"Transportation": ["BC Public Service"],
"Environment": ["Environment Canada and Climate Change"],
"Education": [
"University of Victoria",
"University of Waterloo",
"Wilfrid Laurier University"
]
}
@staticmethod
def mission() -> str:
return "Share latest ideas on data products while documenting knowledge growth"
# Instantiate Yangming Li
me = YangmingLi()
print(f"Roles: {me.roles()}")
print(f"Experience across {len(me.industry_experience())} industries")
print(f"Mission: {me.mission()}")
My Favorite Quote
"You know who the best managers are? They're the great individual contributors, who never ever want to be a manager, but decide they have to be manager because no one else is going to be able to do as good a job as them."
Courses Learned View All Certificates
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.
Yangming's AI/ML Blog
Yangming's Product Blog
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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.
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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.
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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
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[Statistics] Choosing the Right Statistical Test for Survey Analysis
[BI] A comprehensive guide to statistical tests for analyzing survey data
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[Data Engineering] Understanding Databricks: A Comprehensive Guide with Real-World Examples
[Data Engineering] A deep dive into Databricks features and implementation with practical examples
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[Engineering] Understanding Kubernetes: A Comprehensive Guide
[Engineering] A deep dive into Kubernetes architecture, components, and practical implementation
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[Engineering] Introduction to Polars: A Fast and Efficient Data Processing Library
A comprehensive guide to using Polars for high-performance data processing in Python
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[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
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[Engineering] Deep Learning Engineering with JAX: A Modern Approach
A comprehensive guide to using JAX for high-performance machine learning and numerical computing
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[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
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[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
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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.
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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.
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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.
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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.
AI Art Gallery
A collection of AI-generated artwork showcasing the possibilities of artificial intelligence in creative expression.






















These images were created using AI technology, specifically Stable Diffusion. They demonstrate the creative possibilities of artificial intelligence in generating unique visual content.
Yangming's Thoughts
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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.
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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.
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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.
Play Ground - AI Slot Machine
Try your luck with our AI-themed slot machine! Match three symbols to win!









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AI Pioneers & Payouts:
"Godfather of AI"
x5 = 50
Facebook AI
x5 = 75
ML Educator
x5 = 100
Deep Learning Pioneer
x5 = 150
Innovation
x5 = 200
NVIDIA CEO
WILD
BONUS
Contact Information
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Email: yangmingml@yahoo.com
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LinkedIn: Yangming Li
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GitHub: GitHub
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Google Scholar: Google Scholar Profile
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Location: Vancouver, BC, Canada
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Book a Meeting
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