Summary

This chapter has been all about best practices for when you write your own Python packages for your ML solutions. We went over some of the basic concepts of Python programming as a refresher before covering some tips and tricks and good techniques to bear in mind. We covered the importance of coding standards in Python and PySpark. We then performed a comparison between object-oriented and functional programming paradigms for writing your code. We moved onto the details of taking the high-quality code you have written and packaging it up into something you can distribute across multiple platforms and use cases. To do this, we looked into different tools, designs, and setups you could use to make this a reality. This included a brief discussion of how to find good use cases for packaging up. We continued with a summary of some housekeeping tips for your code, including how to test, log, and monitor in your solution. We finished with a brief philosophical point on the importance...