Selecting a Portfolio Project: How to Choose What to Build and Start
For many learners, the hardest part of a portfolio or capstone project is not the implementation. It is choosing what to build.
People often postpone the decision because they want a better idea. They wait until they understand the tools better. They look for something more original or more impressive. As a result, they keep watching lessons, collecting examples, and thinking about projects without actually starting one.
This workshop is designed to help you avoid that trap.
We'll focus on how to choose a project that is realistic, useful, and good enough to start.
We'll discuss what makes a good AI portfolio project, why starting with technology usually leads to weak ideas, and how to work backward from real problems instead.
You'll learn how to check whether your idea can go through the full engineering process: proof of concept, testing, debugging, monitoring, usage data, and evaluation.
By the end of the workshop, you should have a clearer project direction and a practical next step for turning it into a finished portfolio asset.
What we’ll cover:
- Why project selection blocks so many learners - How to start from a real problem instead of a technology - What makes a good capstone or portfolio project - How to choose a project that fits your skills, goals, and time - How to scope the first version so you can finish it - How to use AI tools to interview yourself and generate project ideas - How to move from a vague idea to a 2-3 sentence project description - What to do when you still feel stuck
Hosted by
Alexey Grigorev
Chief Agent Officer at AI Shipping Labs
Software engineer and machine learning practitioner with 15+ years of experience building production ML systems. I focus on practical, production-grade ML and AI systems, from early prototypes to reliable systems in production.
I'm the founder of DataTalks.Club, a free community that connects tens of thousands of practitioners worldwide, and the creator of the Zoomcamp series, free, code-first programs that have reached 100,000+ learners globally.
At AI Shipping Labs, I'm building the kind of environment that would have accelerated my own career growth. After years of teaching at scale, I wanted something more focused: a space for action-oriented builders who want to turn AI ideas into real projects. The community gives members the structure, accountability, and peer support to ship practical AI products consistently, even alongside their main jobs.