Just like traditional software development, ML application development requires the mastery of specialist boilerplate code and a development environment that allows the developer to proceed at a pace that has the lowest amount of friction and distraction. Software developers typically waste a lot of time with basic setup and data wrangling tasks. Being a productive and professional ML developer requires the ability to quickly prototype solutions; this means expending as little effort as possible on trivial tasks.
In the previous chapter, we outlined the main ML problems and a development process that you can follow to obtain a commercial solution. We also explained the advantages offered by Go as a programming language when creating ML applications.
In this chapter, we will guide you through the steps that are required to set up a development...
