{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"AI Lifecycle — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">AI Lifecycle</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">The full sequence of stages through which an AI system moves, from problem framing and data collection through training, evaluation, deployment, monitoring, and eventual retirement or replacement. Thinking in terms of an AI lifecycle helps make clear that building a model is only one part of operating an AI system responsibly and effectively.</p><a href=\"https://gaks.ai/glossary/ai-lifecycle\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/ai-lifecycle →</a></div>"}