Learning Resources & Practical Guidance
I share practical, production-first resources for building AI systems. My approach focuses on real-world application and engineering tradeoffs, not academic theory.
Whether you are a software engineer transitioning to AI, a data scientist learning MLOps, or an ML engineer building production deployment skills, this page outlines a self-directed learning structure that can help.
Learning happens through doing. I share guidance, patterns, code reviews, and debugging approaches as you build real systems.
Who This Is For
Software Engineers
Transitioning from backend or full-stack development to AI/ML engineering. You understand systems but need practical AI experience.
Data Scientists
Moving from notebooks and experiments to production ML systems. You know models but need deployment and infrastructure skills.
ML Engineers
Scaling from prototypes to production systems. You deploy models and want a stronger structure for monitoring, reliability, and cost optimization.
Technical Leaders
Building AI teams or evaluating AI projects. You want practical frameworks for team structure, tooling, and realistic timelines.
Learning Structure
Self-Directed Project Practice
Pick a small project, define constraints, and iterate in short cycles. Use code review checklists, system design templates, and debugging notes to build habits that transfer to production work.
Technical Sessions
Suggested topics to study in depth: RAG systems, LLM deployment, and MLOps pipelines. Pair reading with practical exercises and small experiments to validate understanding.
Learning Paths
Structured roadmaps from beginner to advanced levels. Paths include project milestones, recommended resources, and checkpoint reviews to track progress.
What to Expect
This is not passive learning. You will write code, debug errors, and (when relevant) deploy systems. The goal is to build judgment through practice—you do the work and learn by iterating.
- Expect to spend 10–15 hours per week on projects
- Expect to encounter failures and learn from debugging
- Expect to build systems you can show in portfolios or interviews
- Expect honest feedback, not validation
If you prefer theory-heavy courses or certification programs, this may not be the right fit. The focus is practical engineering for people who want to build production systems.
Get Started
Start with a small project and one clear objective. If you have feedback on this page or want to suggest a topic, you can use the contact page. Helpful context to include:
- Your current experience level and role
- What you want to build or learn
- Timeline and commitment availability