model versioning

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A concise guide to AI model management across the ML lifecycle: organizing and versioning data, tracking experiments, training and tuning, registering models, automating CI/CD, deploying safely, and monitoring for drift, bias, and performance. It stresses governance, documentation, and responsible AI, using MLOps practices and cross‑functional collaboration to keep models reliable, scalable, and compliant.