This chapter will cover the strategy of building a data analysis pipeline and deploying it to run in production on future, incoming data. It will also cover persistent model storage, which is required to distribute for deployment. This chapter will then cover the specific consequences that Python's interpreted nature has on deployment.
The following topics will be covered in this chapter:
- Pipelining your analysis
- Storing a model for deployment
- Loading a deployed model
- Python-specific deployment concerns
