Building an AI model involves a series of systematic steps, starting with defining the problem and gathering relevant data. The data is preprocessed to ensure quality, followed by selecting an appropriate algorithm for the task. Developers train the model using labeled datasets, optimizing its parameters through techniques like gradient descent. Once trained, the model is validated against test data to measure accuracy and identify potential improvements. Deployment involves integrating the model into applications, followed by monitoring its performance in real-world scenarios to refine outputs and ensure reliability.
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