AI Learning Programs Built for Real-World Application
We design our programs around one simple idea: what you learn should translate directly into what you can build. Our approach to teaching generative AI focuses on practical skills that address actual challenges in development, content creation, and system design. Students work through scenarios they'll encounter in their careers—not theoretical exercises.
Foundations Track
This track introduces core concepts without assuming prior AI experience. You'll start by understanding how generative models work, then move into hands-on projects that demonstrate real applications.
- Understanding transformer architectures and their practical uses
- Prompt engineering techniques for different model types
- Building basic AI-assisted workflows in your existing projects
- Evaluating model outputs and refining results
Advanced Integration Track
Once you've got the basics down, this track explores how to integrate AI capabilities into larger systems. We cover API implementation, fine-tuning approaches, and performance considerations.
- Designing systems that combine multiple AI models effectively
- Managing costs and latency in production environments
- Fine-tuning models for specific Walenilora applications
- Building retrieval-augmented generation systems
How the Program Unfolds
Each phase builds on what came before, so you're always working with concepts you understand while pushing into new territory.
Initial Assessment and Direction
We start by figuring out where you are and where you want to go. This isn't a standardized test—it's a conversation about your background and what you're hoping to accomplish. Based on that, we map out which modules make sense for your situation.
Core Concepts and First Projects
You'll spend the first few weeks getting comfortable with how these models actually work. The projects start simple—maybe building a content generation tool or experimenting with different prompting strategies. The goal is to develop intuition for what works and why.
Integration and System Design
This is where things get interesting. You'll take what you've learned and start incorporating it into more complex systems. Maybe that's adding AI capabilities to an existing application, or designing a new workflow that leverages multiple models together.
Specialization and Portfolio Development
The final phase focuses on depth in areas that matter to you. Some students build sophisticated RAG systems, others explore fine-tuning for niche applications. You'll complete a substantial project that demonstrates your capabilities—something you'd actually want to show potential employers or clients.
Learning From People Who Actually Build This Stuff
Our instructors aren't just teachers—they're practitioners who work with these technologies daily. That means you're learning current techniques, not outdated academic approaches.
They've dealt with the same frustrations you'll encounter: models that don't behave as expected, APIs that hit rate limits at the worst times, and projects that need to balance capability with budget constraints. That practical knowledge makes a difference when you're trying to figure out why something isn't working.
Kieran Bjornstad
Systems Architecture
Previously led ML infrastructure at a Toronto fintech company
Solveig Ruddock
Applied AI Development
Built content generation systems for Vancouver media companies
Astrid Pembroke
Model Integration
Specialized in fine-tuning workflows for healthcare applicationsWhat You'll Actually Be Able to Build
The point of all this isn't just to understand AI—it's to use it effectively. By the time you finish, you should be able to tackle real projects with confidence.
Students have built everything from custom coding assistants to content management systems that automatically generate and refine documentation. The specific project matters less than developing the judgment to know which tools fit which problems.