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AI Project Lifecycle
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Data Preparation
Clean the data, perform feature selection/engineering, and split the data into training and testing sets.
Data Collection
Gather datasets, assess data quality, and ensure data is labeled accurately for supervised learning tasks.
Modeling
Select a suitable algorithm, train the model on the dataset, and tune hyperparameters to optimize performance.
Evaluation
Assess model performance using appropriate metrics, perform validation, and identify areas for improvement.
Deployment
Put the finalized model into a production environment, monitor its performance, and prepare for maintenance.
Problem Definition
Identify the business problem, define the AI use case, and prepare the project proposal.
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