This pipeline typically includes the following stages:
Data Collection: Gathering raw data from various sources.
Data Preprocessing: Cleaning and transforming data into a usable format.
Feature Engineering: Creating features that will be used by the AI model.
Model Training: Training the AI model using the prepared data.
Model Evaluation: Assessing the model's performance and making necessary adjustments.
Model Deployment: Deploying the trained model into a production environment.
Monitoring and Maintenance: Continuously monitoring the model's performance and updating it as needed.
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