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It took an engineer 2.5 years to move to Google's AI team. Here are 11 books that helped him adapt to his new role

Rahul Kasanagottu, 32, spent 2.5 years upgrading his skills to move into an artificial intelligence role at Google. To make this dramatic career change, he started reading about AI and machine learning while on parental leave.

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It took an engineer 2.5 years to move to Google's AI team. Here are 11 books that helped him adapt to his new role

Rahul Kasanagottu, 32, spent 2.5 years upgrading his skills to move into an artificial intelligence role at Google. To make this dramatic career change, he started reading about AI and machine learning while on parental leave.

"I worked as a customer service engineer at Google for a few years, but when generative AI came along, I knew I wanted to do it. My daughter was born in April 2023, and that's when the AI ​​boom happened. Google offers generous parental leave, and I thought it would be a good opportunity to spend time with my daughter and start reading books about AI," Casanagottu told Business Insider.

It took the IT guy about 2.5 years, 11 books, and many hours of watching videos to change his career direction.

“I interviewed for four or five different roles, and six months ago I moved from being a senior technical account manager to being a Google Cloud customer engineer specializing in AI and machine learning. In this role, I create demos and show customers how to use Google’s AI products,” said Casanagottu.

Here are 11 books that helped improve your skills

  1. "Artificial Intelligence Engineering", Chip Huyen. Full title in English - AI Engineering: Building Applications with Foundation Models.
  2. "Designing Machine Learning Systems," by Chip Huyen. Full title in English: Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications.
  3. "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems," by Martin Kleppman. Full title in English: Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems.
  4. "Hands-On Large Language Models: Language Understanding and Generation," by Jay Alammar and Maarten Grootendorst. Full title in English: Hands-On Large Language Models: Language Understanding and Generation.
  5. "Generative AI on AWS," by Chris Fregley, Shelby Eigenbrod, and Ange Barth. Full title in English: Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications.
  6. "Deep Learning" by Ian Goodfellow, Joshua Bengio, Aaron Courville. Full title in English is Deep Learning (Adaptive Computation and Machine Learning series).
  7. "Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World" by Marco Yansiti and Karima R. Lakhani. Full title in English — Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
  8. "Prediction Machines" by Ajay Agrawal, Joshua Gans, and Avi Goldfarb. Full title in English is Prediction Machines: The Simple Economics of Artificial Intelligence.
  9. "Power and Prediction" by Ajay Agrawal, Joshua Gans, Avi Goldfarb. Full title in English is Power and Prediction: The Disruptive Economics of Artificial Intelligence.
  10. "Genesis" by Henry Kissinger, Craig Mundy, and Eric Schmidt. Full title in English is Genesis: Artificial Intelligence, Hope, and the Human Spirit.
  11. "Deep Medicine" by Eric Topol. Full title in English - Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.

The books are available on Amazon, and some can also be ordered on the Ukrainian Yakaboo (delivery from the UK).

“Chip Huen’s two books were my favorites. He explains things in a very accessible way and opened up my understanding of how organizations use and implement AI. At first, it was difficult to understand the difference between the research and applied sides of AI. These books helped me realize that my interest lies in applied AI,” the IT professional added.

The book, Generative Artificial Intelligence on AWS, has a companion course on deep learning, which was very helpful to Casanagott in his early days.

“‘Power and Prediction’ was another favorite. It talks about how technology must scale economically to be meaningful. For example, if a light bulb still cost thousands of dollars, it wouldn’t be electrifying every home today. The books talk about AI in similar terms,” the engineer explained.

In his opinion, the book “Genesis” also stood out. It talks about the future of AI and the challenges it poses.

“Books don’t usually have assignments, but courses have a lot of hands-on exercises. Over time, I realized that this was missing from my resume. It was becoming increasingly difficult to convince hiring managers that I could do the job because it required creating demos and doing hands-on projects. I realized that I had to do my own projects, and AI tools were incredibly helpful for that,” Casanagottu noted.

The IT expert also advised being persistent in learning: “It takes time to understand complex problems, and sometimes you have to read the same thing over and over again to fully understand the concept. Many people who want to get into the field of artificial intelligence, including me, rush to get a job in the field of artificial intelligence after six months. But many machine learning concepts take time to master. Therefore, persistence is necessary.”

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What are IT people reading? What books were sold the most during DOU Day Picnic 2025
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