12 Reporting and logging with Python
This chapter covers
- Creating automated reports
- Sending emails with Python
- Using the logging module to monitor running code
So far we’ve covered many of the key elements of building and deploying a machine learning pipeline into production, including better structuring your code, scaling, testing, and scheduling. Another piece of the pipeline that is very important is monitoring. Monitoring can be considered in several contexts:
- Automated reports
- Sending notifications, including email alerts, when certain events occur, such as pipeline completion or model failure
- Logging events related to code execution to help with debugging issues