> 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

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Lifecycle - ML Projects
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* 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.