Lifecycle
What is the lifecycle of a machine learning project?
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.
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