# Monitoring {% embed url="" %} Monitoring - Testing and Deployment {% endembed %} ## Summary * It is crucial to monitor serving systems, training pipelines, and input data. A typical monitoring system can **raise alarms** when things go wrong and provide the records for tuning things. * Cloud providers have decent monitoring solutions. * Anything that can be logged can be monitored: dependency changes, distribution shift in data, model instabilities, etc. * **Data distribution monitoring** is an underserved need! * It is important to monitor the **business uses** of the model, not just its statistics. Furthermore, it is important to be able to **contribute failures** back to the dataset. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/monitoring.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.