# Project Structure {% embed url="" %} Project Structure - Testing and Deployment {% endembed %} ## Summary * The **prediction system** involves code to process input data, to construct networks with trained weights, and to make predictions. * The **training system** processes raw data, runs experiments, and manages results. * The goal of any prediction system is to be deployed into the **serving system**. Its purpose is to serve predictions and to scale to demand. * **Training and validation data** are used in conjunction with the training system to generate the prediction system. * At production time, we have **production data** that has not been seen before and can only be served by the serving system. * The prediction system should be tested by **functionality** to catch code regressions and by **validation** to catch model regressions. * The training system should have its tests to catch upstream regressions (change in data sources, upgrade of dependencies) * For production data, we need **monitoring** that raises alert to downtime, errors, distribution shifts, etc. and catches service and data regressions. --- # 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/project-structure.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.