Cohort 1

Sr. Machine Learning Engineer

Ignacio Gaitán

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building, training, and deploying machine learning models — from classical ML to deep learning.

Designing scalable, reliable software systems — architecture patterns, distributed systems, and technical tradeoffs.

Learn to design and evaluate a production-ready media recommender: metrics, architecture, cold start, and drafting an effective roadmap.

Principal ML Engineer @ Disney, ex-Meta

The New York Times

The Walt Disney Company

ESPN

Columbia University

Katerina Zanos

Principal ML Engineer @ Disney, x-Meta, x-NYT

Production-Ready Recommenders for Media Products

<h3>Why I enrolled</h3>
<p class="p2-tall">I want to rigorously challenge the practical intuition I’ve built over two years working on recommender systems for streaming platforms like Hulu and ESPN by grounding it more deeply in theory. I’m also looking to pressure test my assumptions and biases against the experiences of others applying recommender systems in different product contexts, so I can expand and refine my mental models beyond the use cases I've faced.</p>
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<h3>What I’ll apply</h3>
<p class="p2-tall">Applying a clearer metrics-first approach to recommender system design, especially defining a proper metrics spec upfront to align product and engineering decisions before model work begins.</p>
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Certificate

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