Chip Huyen Portfolio
Author of the #1 Amazon bestseller "Designing Machine Learning Systems" and 2025's most-read O'Reilly title "AI Engineering"; her site hosts a free, widely-cited MLOps guide with real production case studies from engineers at major tech companies.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Systems-design framing over model-only framing
Structuring her work around designing ML systems (data, serving, monitoring, feedback loops) matches what senior ML engineering interviews actually test.
Sourced case studies from working engineers, not hypotheticals
The MLOps guide includes real case studies from practitioners who have deployed the systems discussed, requiring real standing in the field to collect.
A maintained, evolving public tools/landscape reference
Continually updating a running list of MLOps tools demonstrates active tracking of the ecosystem rather than a portfolio frozen at one point in time.
What ML Engineers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Frame your portfolio around systems and lifecycle, not just modeling.
If possible, get case studies or quotes from people who have run your approach in production.
Keep a living reference you update regularly, not a one-time artifact.
Write about monitoring, data drift, and failure modes, not just successful launches.
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