Books¶

A journey into a career in AI/ML is not complete without the support of great books. Here are the ones I've selected to read. Checkout the DS/ML Book Club for more recommendations.

Currently reading¶

• Writing for Developers by Piotr Sarna and Cynthia Dunlop, November 2024.
  ISBN 9781633436282 ⇨ Manning Publications.

My backlog¶

Interview preparation¶

• Ace the Data Science Interview 201 Real Interview Questions Asked by FAANG, Tech Startups, & Wall Street, by Kevin Huo and Nick Singh, August 2021.
  ISBN: 978-0578973838 ⇨ Google Books

Machine Learning¶

• Machine Learning Engineering in Action, Ben Wilson, March 2022.
  ISBN 9781617298714 ⇨ Manning Publications.

Machine Learning Systems¶

• Designing Machine Learning Systems An Iterative Process for Production-Ready Applications, by Chip Huyen, May 2022.
  ISBN 9781098107963 ⇨ O'Reilly Media.

• AI Engineering Building Applications with Foundation Models, by Chip Huyen, December 2024. ISBN 9781098166304 ⇨ O'Reilly Media.

Leadership and Career¶

• Build a Career in Data Science, by Emily Robinson and Jacqueline Nolis, March 2020.
  ISBN 9781617296246 ⇨ Manning Publications.

• How to Lead in Data Science, by Jike Chong and Yue Cathy Chang, November 2021.
  ISBN 9781617298899 ⇨ Manning Publications.

• Managing Machine Learning Projects, by Simon Thompson, March 2023.
  ISBN 9781633439023 ⇨ Manning Publications.

• Lead Developer Career Guide, by Shelley Benhoff, October 2024.
  ISBN 9781633438071 ⇨ Manning Publications.