Summary
In this chapter, we discussed how SREs take and give directions based on insights and knowledge extracted from data. We outlined how they resolve complex problems by troubleshooting them with a scientific method. Together, we understood the most used statistical methods to analyze observability data to uncover patterns, trends, and anomalies.
Additionally, we acquired knowledge of other mathematical models SREs can apply to datasets. We consolidated the knowledge from this chapter by going through the practical simulation lab available on GitHub. Finally, we showed how to further develop your data science skill set.
In the next chapter, we will discuss reliable architectures and how to apply strategies to have good system designs.
