> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects/lifecycle.md). # Lifecycle {% embed url="" %} Lifecycle - ML Projects {% endembed %} * Phase 1 is **Project Planning and Project Setup**: At this phase, we want to decide the problem to work on, determine the requirements and goals, as well as figure out how to allocate resources properly. * Phase 2 is **Data Collection and Data Labeling**: At this phase, we want to collect training data (images, text, tabular, etc.) and potentially annotate them with ground truth, depending on the specific sources where they come from. * Phase 3 is **Model Training and Model Debugging**: At this phase, we want to implement baseline models quickly, find and reproduce state-of-the-art methods for the problem domain, debug our implementation, and improve the model performance for specific tasks. * Phase 4 is **Model Deployment and Model Testing**: At this phase, we want to pilot the model in a constrained environment, write tests to prevent regressions, and roll the model into production. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## 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, and the optional `goal` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects/lifecycle.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.