What End-to-End AI Engineering Really Means

Hosted by Aurimas Griciūnas

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End-to-End AI Engineering Bootcamp
Aurimas Griciunas
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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)

Aurimas Griciūnas is a recognized AI expert, LinkedIn Top Voice in AI, and the founder of SwirlAI. He previously served as Chief Product Officer at Neptune.ai where he worked closely with top ML teams to scale infrastructure, evaluation, and LLMOps practices across industries. With over a decade of experience at the intersection of data science, machine learning, and software engineering, Aurimas has led AI initiatives in both startups and enterprise environments. His mission is to bridge the gap between hype and reality by teaching engineers how to build systems that work in the real world. Students will benefit from his hands-on knowledge, technical depth, and product-first mindset - gained by solving actual engineering problems.

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