About
Leonard Zhu
I am a full-time IT infrastructure professional with experience in enterprise and cloud architecture and operations. AI is where that work is going, so this site documents the projects, courses, and reading along the way.
Path
- Enterprise and cloud infrastructure Day-to-day work in architecture and operations: the systems that have to stay up before a model is useful.
- Applied AI practice Hands-on projects — agents, edge devices, small model apps — and a public record of what I am learning. See Work.
- Doctor of Technology research Research interest: how AI technologies and infrastructure change business models and competitive strategy.
Research interest
The focus is the role of AI technologies and infrastructure in transforming business models. AI here is not only an efficiency tool. It pushes organizations to rethink how they operate and compete.
I look at how AI-driven insight leads to new models, how organizations try to gain an edge, and what integration does to market dynamics. The aim is a practical frame for using the technology with some care, not a claim that the frame is finished.
The engineering side is reading papers, trying techniques, and working with data and training loops. The point of the research is to connect that technical potential with the practical realities of implementing it in a business.
What this site is for
- Notes on AI, machine learning, and the infrastructure underneath them.
- A record of moving from IT infrastructure toward applied AI.
- Courses, books, and tools I am actually using.
- Projects that show the work, rather than a services brochure.
Questions
What motivates the AI work?
The chance to take hard problems — or ordinary ones — and make them a bit more solvable. Living simply and enjoying the work is part of it.
Which technical skills keep showing up?
The field crosses infrastructure, data, machine learning, and software. A few skills travel across those roles:
- Python first, plus Java, Scala, or SQL where the system needs them.
- Data structures and algorithms: lists, trees, graphs, sorting, search, optimization.
- Git, databases, and how data is stored and retrieved.
- Enough machine learning to know when a model belongs in the system, and ordinary software design (objects, functions, patterns).
- DevOps habits: CI/CD, Docker, Kubernetes.
- Cloud services on AWS, Azure, or Google Cloud.
How would you start if you share the interest?
Start small. Courses, a degree, or a short program can supply the vocabulary. Then build something — a personal project or an open-source contribution. Follow papers and books, because the field moves. It is a long practice, not a sprint.
How do you keep going?
Momentum is something I have to restart on purpose. A weekly course, a small commit, or a page of notes beats waiting to feel ready.
Are the courses and books sponsored?
No. I am not affiliated with, sponsored by, or endorsed by the training publishers or authors featured here. They are listed because I am using them. Names and marks belong to their owners.