Free Lesson
What End-to-End AI Engineering Really Means
Part of AI Engineering in Practice: Role, Skills, Industry Reality.
45 min
Feb 20, 2026 11:00 AM
Virtual (Zoom)
In this video
What you'll learn
End-to-End Ownership of AI Systems
What it means to own an AI system from problem framing through deployment, evaluation, and long-term operation.
Designing AI Systems Under Real Constraints
How latency, cost, reliability, data quality, evaluation, and risk shape architecture choices in production AI systems.
Managing Change and Failure in Production AI
How AI engineers handle drift, regressions, updates, and failures through evals, monitoring, and controlled iteration.
Why this topic matters
AI Engineering has become an overloaded term, shaped by rapidly evolving tooling, fragmented infrastructure and inconsistent role boundaries. As the discipline evolves, teams risk misalignment and fragile systems. Establishing a shared understanding of what End-to-End AI Engineering is, and what it is not, is essential for building AI systems that survive beyond demos and scale in production.
You'll learn from
Aurimas Griciūnas
Founder & CEO @ SwirlAI | Former CPO @ Neptune.ai (acquired by OpenAI)