The model life cycle encompasses all stages from data collection, preprocessing, model development, evaluation, deployment, to ongoing monitoring and maintenance. AI/ML engineers manage this entire process to ensure models are scalable, reliable, and effective in real-world applications. Understanding the life cycle helps prevent issues like model drift and ensures continuous improvement.
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