So far in this book, we have focused on how to prepare and use ML algorithms in Go. This included the preparation of data in Chapter 2, Setting Up the Development Environment, and the use of data to build models in Chapter 3, Supervised Learning, and Chapter 4, Unsupervised Learning. We also looked at how to integrate an existing ML model into a Go application in Chapter 5, Using Pretrained Models. Finally, we covered how to integrate ML into production systems in Chapter 6, Deploying Machine Learning Applications. To conclude, we will take a look at the different stages in a typical project, and how to manage the end-to-end process of developing and deploying a successful ML system.
AI expert Andrej Karparthy has written[1] about how ML can be used to simplify what were previously very complex systems. Often, it is simpler to allow a machine...