Target audience

If you’ve picked up this book because you’re interested in working with one of the key emerging technologies of the 2020s but not sure where to start, you’re in the right place. Whether you want to become an ML engineer, a platform engineer, a research scientist, or you want to do ML but don’t yet know the differences among those titles, I hope that this book will give you some useful pointers.

This book focuses more on roles involving machine learning production than research, not because I believe production is more important. As fewer and fewer companies can afford to pursue pure research whereas more and more companies want to adopt machine learning, there will be, and already are, vastly more roles involving production than research.

This book was written with two main groups of candidates in mind:

  1. Recent graduates looking for their first full-time jobs.
  2. Software engineers and data scientists who want to transition into machine learning.

I imagine the majority of readers of this book come from a computer science background. The second part of the book, where the questions are, is fairly technical. However, as machine learning finds its use in more industries -- healthcare, farming, trucking, fashion, you name it -- the field needs more people with diverse interests. If you’re interested in machine learning but hesitant to pursue it because you don’t have an engineering degree, I strongly encourage you to explore it. This book, especially the first part, might address some of your needs. After all, I only took an interest in matrix manipulation after working as a writer for almost a decade.

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