It seems like machine learning and artificial intelligence is all the rage, both in hip tech companies and increasingly in larger enterprise companies. Data scientists are using machine learning to do everything from drive cars to draw cats. However, if you follow the data science community, you have very likely seen something like language wars unfold between Python and R users. These languages dominate the machine learning conversation and often seem to be the only choices to integrate machine learning in your organization. We will explore a third option in this book: Go, the open source programming language created at Google.
The unique features of Go, along with the mindset of Go programmers, can help data scientists overcome some of the common struggles that they encounter. In particular, data scientists are (unfortunately) known to produce bad, inefficient, and unmaintainable code. This book will address this issue, and will clearly show you how to be productive in machine learning while also producing applications that maintain a high level of integrity. It will also allow you to overcome the common challenges of integrating analysis and machine learning code within an existing engineering organization.
This book will develop readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book will clearly introduce the technical, programming aspects of machine learning in Go, but it will also guide the reader to understand sound workflows and philosophies for real-world analysis.
