From Astronomy to Applied ML

​In this episode, we’re joined by Daniel Egbo, an astrophysicist turned ML engineer and AI ambassador (Arize, Tavily). Daniel will talk about the moment he decided to try data science and ML and what he transferred from astronomy.

​We’ll dive into how he selects resources, stays motivated during self-learning, and overcomes obstacles.

​We plan to cover:

• ​Why Daniel decided to try data science and ML and skills from astronomy that helped

• ​How he chooses learning resources in a noisy landscape

• ​What keeps him motivated and the first steps when he feels stuck

• ​Building a career from Africa/remotely

• ​Daniel’s experience with ML Zoomcamp

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​About the speaker

​Daniel Egbo is an astrophysicist turned machine learning engineer and AI ambassador (Arize, Tavily). A PhD candidate at the University of Cape Town, he builds end-to-end ML and LLM applications with a focus on reliability and learning in public. His work spans knowledge-retrieval assistants, practical evaluation, and applying data science to astronomy.

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