This book is a book on ML using Go. Go is a rather opinionated programming language. There's the Go way, or no other way at all. This may sound rather fascist, but it has resulted in a very enjoyable programming experience. It also makes working in teams rather efficient.

Further, Go is a fairly efficient language when compared to Python. I have moved on almost exclusively to using Go to do my ML and data science work.

Go also has the benefit of working well cross-platform. At work, developers may choose to work on different operating systems. Go works well across all of them. The programs that are written in Go can be trivially cross-compiled for other platforms. This makes deployment a lot easier. There's no unnecessary mucking around with Docker or Kubernetes.

Are there drawbacks when using Go for ML? Only as a library author. In general, using Go ML libraries is painless. But in order for it to be painless, you must let go of any previous ways you programmed.