11 Documentation and deployment

 

This chapter covers

  • Producing effective milestone documentation
  • Managing project history using source control
  • Deploying results and making demonstrations

In this chapter, we’ll survey techniques for documenting and deploying your work. We will work specific scenarios, and point to resources for further study if you want to master the techniques being discussed. The theme is this: now that you can build machine learning models, you should explore tools and procedures to become proficient at saving, sharing, and repeating successes. Our mental model (figure 11.1) for this chapter emphasizes that this chapter is all about sharing what you model. Let’s use table 11.1 to get some more-specific goals in this direction.

Figure 11.1. Mental model

11.1. Predicting buzz

11.2. Using R markdown to produce milestone documentation

11.2.1. What is R markdown?

11.2.2. knitr technical details

11.2.3. Using knitr to document the Buzz data and produce the model

11.3. Using comments and version control for running documentation

11.3.1. Writing effective comments

11.3.2. Using version control to record history

11.3.3. Using version control to explore your project

11.3.4. Using version control to share work

11.4. Deploying models

11.4.1. Deploying demonstrations using Shiny

11.4.2. Deploying models as HTTP services

11.4.3. Deploying models by export

11.4.4. What to take away