Summary

This chapter has discussed ethical issues surrounding ML, which include sanitizing data and ensuring that raw data remains secure. Many developers view this process as unnecessarily complicated and therefore avoid it at all costs. However, addressing ethical issues in data management also yields significant benefits to everyone involved in working with the data and associated ML models. The goal is to ensure that any analysis you make is both fair and secure.

Congratulations! You’ve made it to the end of the book. By now you’ve been introduced to a lot more than just security issues, and have addressed a wide range of data management and model creation issues that ultimately affect the results you receive from any data analysis. Ultimately, it doesn’t matter whether you’re working with text, graphics, sounds, or other data types; the result you obtain reflects the usefulness of the process you use to obtain it.