# 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.