{"id":71,"date":"2026-04-19T11:38:54","date_gmt":"2026-04-19T02:38:54","guid":{"rendered":"https:\/\/ai-cloud.kr\/?p=71"},"modified":"2026-04-19T11:38:55","modified_gmt":"2026-04-19T02:38:55","slug":"ai-%eb%aa%a8%eb%8d%b8-%ec%84%a0%ed%83%9d-%ea%b8%b0%ec%a4%80-%eb%b3%80%ed%99%94-%ec%84%b1%eb%8a%a5%eb%b3%b4%eb%8b%a4-%ec%9a%b4%ec%98%81%eb%b9%84%ea%b0%80-%ec%a4%91%ec%9a%94%ed%95%b4%ec%a7%80%eb%8a%94","status":"publish","type":"post","link":"https:\/\/ai-cloud.kr\/?p=71","title":{"rendered":"AI \ubaa8\ub378 \uc120\ud0dd \uae30\uc900 \ubcc0\ud654: \uc131\ub2a5\ubcf4\ub2e4 \uc6b4\uc601\ube44\uac00 \uc911\uc694\ud574\uc9c0\ub294 \uc21c\uac04(A Shift in AI Model Selection: The Moment When Operating Cost Becomes More Important Than Performance)"},"content":{"rendered":"<h2>AI \ubaa8\ub378 \uc120\ud0dd, \uacfc\uac70\uc640 \ud604\uc7ac\uc758 \ucc28\uc774: \uc131\ub2a5 \uc911\uc2ec\uc5d0\uc11c \ube44\uc6a9 \ud6a8\uc728\uc131\uc73c\ub85c<\/h2>\n<p>\uacfc\uac70 AI \ubaa8\ub378\uc744 \uc120\ud0dd\ud560 \ub54c\ub294 \ubb34\uc870\uac74 &#8216;\uc131\ub2a5&#8217;\uc774 \ucd5c\uace0\uc600\uc2b5\ub2c8\ub2e4. \ub354 \uc815\ud655\ud558\uace0, \ub354 \ube60\ub974\uace0, \ub354 \ub611\ub611\ud55c \ubaa8\ub378\uc774 \ucd5c\uace0\ub85c \uc5ec\uaca8\uc84c\uc8e0. \ub9c8\uce58 \uc790\ub3d9\ucc28\ub97c \uc0b4 \ub54c \ucd5c\uace0 \uc18d\ub3c4\ub098 \uc81c\ub85c\ubc31\uc744 \uac00\uc7a5 \uba3c\uc800 \ub530\uc9c0\ub294 \uac83\ucc98\ub7fc\uc694. \ud558\uc9c0\ub9cc \uc774\uc81c AI \uae30\uc220\uc774 \ubc1c\uc804\ud558\uace0 \uc6b0\ub9ac \uc0b6\uc5d0 \uae4a\uc219\uc774 \ub4e4\uc5b4\uc624\uba74\uc11c, AI \ubaa8\ub378 \uc120\ud0dd\uc758 \uae30\uc900\uc774 \uc870\uae08\uc529 \ub2ec\ub77c\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 &#8216;\uc6b4\uc601\ube44&#8217;\ub77c\ub294 \ud604\uc2e4\uc801\uc778 \ubb38\uc81c\uac00 \uc911\uc694\ud558\uac8c \ub5a0\uc624\ub974\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>\uc65c AI \ubaa8\ub378 \uc120\ud0dd\uc758 \uae30\uc900\uc774 \ub2ec\ub77c\uc9c0\uace0 \uc788\uc744\uae4c\uc694?<\/h3>\n<p>AI \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ub370\uc5d0\ub294 \uc0dd\uac01\ubcf4\ub2e4 \ub9ce\uc740 \ube44\uc6a9\uc774 \ub4ed\ub2c8\ub2e4. \ub2e8\uc21c\ud788 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \ub370 \ub4dc\ub294 \ube44\uc6a9\ubfd0\ub9cc \uc544\ub2c8\ub77c, \ubaa8\ub378\uc744 \uc720\uc9c0\ud558\uace0 \uc6b4\uc601\ud558\ub294 \ub370\uc5d0\ub3c4 \uc9c0\uc18d\uc801\uc778 \ube44\uc6a9\uc774 \ubc1c\uc0dd\ud558\uc8e0.<\/p>\n<ul>\n<li>\n<p><strong>\ub370\uc774\ud130 \uc99d\uac00\uc640 \ubcf5\uc7a1\uc131:<\/strong> AI \ubaa8\ub378\uc740 \ud559\uc2b5 \ub370\uc774\ud130\uac00 \ub9ce\uc744\uc218\ub85d \uc131\ub2a5\uc774 \uc88b\uc544\uc9c0\ub294 \uacbd\ud5a5\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ub370\uc774\ud130\uac00 \ub9ce\uc544\uc9c8\uc218\ub85d \uc800\uc7a5\ud558\uace0 \uad00\ub9ac\ud558\ub294 \ub370 \ub4dc\ub294 \ube44\uc6a9\ub3c4 \ub298\uc5b4\ub0a9\ub2c8\ub2e4. \ub610\ud55c, \ubaa8\ub378\uc758 \ubcf5\uc7a1\uc131\uc774 \uc99d\uac00\ud558\uba74\uc11c \ub354 \ub9ce\uc740 \ucef4\ud4e8\ud305 \uc790\uc6d0\uc774 \ud544\uc694\ud558\uac8c \ub418\uace0, \uc774\ub294 \uace7 \uc6b4\uc601\ube44 \uc0c1\uc2b9\uc73c\ub85c \uc774\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc0c1\uc2dc \uc6b4\uc601\uc758 \ud544\uc694\uc131:<\/strong> \ub9ce\uc740 AI \uc11c\ube44\uc2a4\ub294 24\uc2dc\uac04 365\uc77c \uc26c\uc9c0 \uc54a\uace0 \uc791\ub3d9\ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ucc57\ubd07\uc774\ub098 \ucd94\ucc9c \uc2dc\uc2a4\ud15c \uac19\uc740 \uc11c\ube44\uc2a4\ub294 \uc0ac\uc6a9\uc790\uac00 \uc5b8\uc81c\ub4e0 \uc811\uadfc\ud560 \uc218 \uc788\uc5b4\uc57c \ud558\ubbc0\ub85c, \uc11c\ubc84 \uc6b4\uc601 \ubc0f \uc720\uc9c0\ubcf4\uc218 \ube44\uc6a9\uc774 \uafb8\uc900\ud788 \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud074\ub77c\uc6b0\ub4dc \ucef4\ud4e8\ud305 \ube44\uc6a9:<\/strong> AI \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uac70\ub098 \uc6b4\uc601\ud558\uae30 \uc704\ud574 \ud074\ub77c\uc6b0\ub4dc \uc11c\ube44\uc2a4\ub97c \uc774\uc6a9\ud558\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \ud074\ub77c\uc6b0\ub4dc \uc11c\ube44\uc2a4\ub294 \uc0ac\uc6a9\ud55c \ub9cc\ud07c \ube44\uc6a9\uc744 \uc9c0\ubd88\ud558\ub294 \ubc29\uc2dd\uc774\uae30 \ub54c\ubb38\uc5d0, \ubaa8\ub378\uc758 \uc0ac\uc6a9\ub7c9\uc774 \ub298\uc5b4\ub0a0\uc218\ub85d \ube44\uc6a9\ub3c4 \ud568\uaed8 \uc99d\uac00\ud569\ub2c8\ub2e4. \ud2b9\ud788 \ubcf5\uc7a1\ud55c \uc5f0\uc0b0\uc774\ub098 \ub300\uaddc\ubaa8 \ub370\uc774\ud130 \ucc98\ub9ac\uac00 \ud544\uc694\ud55c \uacbd\uc6b0, \uc608\uc0c1\uce58 \ubabb\ud55c \ub192\uc740 \ube44\uc6a9\uc774 \ubc1c\uc0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc9c0\uc18d\uc801\uc778 \uc5c5\ub370\uc774\ud2b8\uc640 \uac1c\uc120:<\/strong> AI \ubaa8\ub378\uc740 \ud55c\ubc88 \ub9cc\ub4e4\uace0 \ub05d\ub098\ub294 \uac83\uc774 \uc544\ub2d9\ub2c8\ub2e4. \uc2dc\uc7a5 \ubcc0\ud654, \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130, \uc0ac\uc6a9\uc790 \ud53c\ub4dc\ubc31 \ub4f1\uc5d0 \ub9de\ucdb0 \uc9c0\uc18d\uc801\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8\ud558\uace0 \uac1c\uc120\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub3c4 \ucef4\ud4e8\ud305 \uc790\uc6d0\uacfc \uc778\ub825\uc774 \ud22c\uc785\ub418\ubbc0\ub85c \ucd94\uac00\uc801\uc778 \ube44\uc6a9\uc774 \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\uc774\ucc98\ub7fc AI \ubaa8\ub378\uc744 &#8216;\ub9cc\ub4dc\ub294 \uac83&#8217;\ub9cc\ud07c\uc774\ub098 &#8216;\uc798 \uc6b4\uc601\ud558\ub294 \uac83&#8217;\uc774 \uc911\uc694\ud574\uc84c\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc774\uc81c\ub294 \uc131\ub2a5\ub9cc \ubcf4\uace0 \ub35c\ucee5 \uc120\ud0dd\ud588\ub2e4\uac00\ub294 \uc608\uc0c1\uce58 \ubabb\ud55c \uc6b4\uc601\ube44 \ud3ed\ud0c4\uc744 \ub9de\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>\uc6b4\uc601\ube44\uac00 \uc131\ub2a5\ubcf4\ub2e4 \uc911\uc694\ud574\uc9c0\ub294 \uc21c\uac04\ub4e4<\/h3>\n<p>\uadf8\ub807\ub2e4\uba74 \uad6c\uccb4\uc801\uc73c\ub85c \uc5b4\ub5a4 \uc0c1\ud669\uc5d0\uc11c AI \ubaa8\ub378\uc758 \uc131\ub2a5\ubcf4\ub2e4 \uc6b4\uc601\ube44\uac00 \ub354 \uc911\uc694\ud55c \uc694\uc18c\uac00 \ub420\uae4c\uc694? \uba87 \uac00\uc9c0 \ub300\ud45c\uc801\uc778 \uc0ac\ub840\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h4>1. \ubc18\ubcf5\uc801\uc774\uace0 \uc77c\uc0c1\uc801\uc778 \uc5c5\ubb34 \uc790\ub3d9\ud654<\/h4>\n<p>\ubc18\ubcf5\uc801\uc774\uace0 \uc77c\uc0c1\uc801\uc778 \uc5c5\ubb34\ub97c \uc790\ub3d9\ud654\ud558\ub294 AI \uc194\ub8e8\uc158\uc744 \ub3c4\uc785\ud560 \ub54c, \uc6b4\uc601\ube44\ub294 \ub9e4\uc6b0 \uc911\uc694\ud55c \uace0\ub824 \uc0ac\ud56d\uc774 \ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uace0\uac1d \ubb38\uc758\uc5d0 \ub300\ud55c \ub2e8\uc21c \ub2f5\ubcc0\uc744 \ucc98\ub9ac\ud558\ub294 \ucc57\ubd07\uc774\ub098, \ubb38\uc11c\uc5d0\uc11c \ud2b9\uc815 \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\ub294 \uc791\uc5c5 \ub4f1\uc774 \uc5ec\uae30\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ucc57\ubd07:<\/strong> \ud558\ub8e8\uc5d0\ub3c4 \uc218\ubc31, \uc218\ucc9c \uac74\uc758 \ub2e8\uc21c \ubb38\uc758\uac00 \ubc18\ubcf5\uc801\uc73c\ub85c \ub4e4\uc5b4\uc628\ub2e4\uba74, \uc774\ub97c \ucc98\ub9ac\ud558\ub294 AI \ucc57\ubd07\uc758 \uc6b4\uc601\ube44\ub294 \uc804\uccb4 \uc2dc\uc2a4\ud15c \ube44\uc6a9\uc5d0\uc11c \uc0c1\ub2f9 \ubd80\ubd84\uc744 \ucc28\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uacbd\uc6b0, \uc544\uc8fc \ub192\uc740 \uc218\uc900\uc758 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ub2a5\ub825\uc744 \uac00\uc9c4 \uace0\uac00\uc758 \ubaa8\ub378\ubcf4\ub2e4\ub294, \ud569\ub9ac\uc801\uc778 \ube44\uc6a9\uc73c\ub85c \uc77c\uc815\ud55c \uc218\uc900\uc758 \ub2f5\ubcc0\uc744 \uc81c\uacf5\ud560 \uc218 \uc788\ub294 \ubaa8\ub378\uc774 \ub354 \ud6a8\uc728\uc801\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc815\ubcf4 \ucd94\ucd9c:<\/strong> \uc815\ud574\uc9c4 \ud615\uc2dd\uc758 \ubb38\uc11c\uc5d0\uc11c \ud2b9\uc815 \ub370\uc774\ud130\ub97c \ucd94\ucd9c\ud558\ub294 AI \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \ub54c\ub3c4 \ub9c8\ucc2c\uac00\uc9c0\uc785\ub2c8\ub2e4. \uc774 \uc791\uc5c5\uc740 \ube44\uad50\uc801 \uc815\ud615\ud654\ub418\uc5b4 \uc788\uc73c\uba70, \uace0\ub3c4\uc758 \ucc3d\uc758\uc131\uc774\ub098 \ubcf5\uc7a1\ud55c \ucd94\ub860 \ub2a5\ub825\uc774 \uc694\uad6c\ub418\uc9c0 \uc54a\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \ucd5c\uc2e0, \ucd5c\uace0 \uc131\ub2a5\uc758 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\ubcf4\ub2e4, \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ucd5c\uc801\ud654\ub418\uace0 \uc6b4\uc601\ube44\uac00 \uc800\ub834\ud55c \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uacbd\uc81c\uc801\uc73c\ub85c \uc720\ub9ac\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\uc774\ub7f0 \uc0c1\ud669\uc5d0\uc11c\ub294 99%\uc758 \uc815\ud655\ub3c4\ub97c \uac00\uc9c4 \ubaa8\ub378\uacfc 95%\uc758 \uc815\ud655\ub3c4\ub97c \uac00\uc9c4 \ubaa8\ub378\uc758 \ucc28\uc774\uac00 \uc2e4\uc81c \ube44\uc988\ub2c8\uc2a4\uc5d0 \ubbf8\uce58\ub294 \uc601\ud5a5\uc740 \ubbf8\ubbf8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc6b4\uc601\ube44\ub294 2\ubc30, 3\ubc30 \uc774\uc0c1 \ucc28\uc774\uac00 \ub0a0 \uc218 \uc788\uc8e0. \uadf8\ub807\ub2e4\uba74 \ub2f9\uc5f0\ud788 \uc6b4\uc601\ube44\uac00 \ub0ae\uc740 \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \ud569\ub9ac\uc801\uc785\ub2c8\ub2e4.<\/p>\n<h4>2. \ub300\uaddc\ubaa8 \uc0ac\uc6a9\uc790 \ub300\uc0c1 \uc11c\ube44\uc2a4<\/h4>\n<p>\uc218\ub9ce\uc740 \uc0ac\uc6a9\uc790\uac00 \ub3d9\uc2dc\uc5d0 \uc811\uc18d\ud558\ub294 \uc11c\ube44\uc2a4\uc5d0\uc11c\ub294 AI \ubaa8\ub378\uc758 \uc6b4\uc601\ube44\uac00 \uc11c\ube44\uc2a4\uc758 \uc9c0\uc18d \uac00\ub2a5\uc131\uc744 \uacb0\uc815\uc9d3\ub294 \uc911\uc694\ud55c \uc694\uc778\uc774 \ub429\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uc18c\uc15c \ubbf8\ub514\uc5b4 \ud53c\ub4dc \ucd94\ucc9c:<\/strong> \ud398\uc774\uc2a4\ubd81, \uc778\uc2a4\ud0c0\uadf8\ub7a8 \uac19\uc740 \uc18c\uc15c \ubbf8\ub514\uc5b4 \ud50c\ub7ab\ud3fc\uc740 \uc218\uc5b5 \uba85\uc758 \uc0ac\uc6a9\uc790\uac00 \uc2e4\uc2dc\uac04\uc73c\ub85c \ucf58\ud150\uce20\ub97c \uc18c\ube44\ud569\ub2c8\ub2e4. \uac01 \uc0ac\uc6a9\uc790\uc5d0\uac8c \ucd5c\uc801\ud654\ub41c \ud53c\ub4dc\ub97c \ucd94\ucc9c\ud558\uae30 \uc704\ud574 AI \ubaa8\ub378\uc774 \ub04a\uc784\uc5c6\uc774 \uc791\ub3d9\ud574\uc57c \ud558\uc8e0. \uc774\ub54c \ubaa8\ub378\uc758 \uc131\ub2a5\ub3c4 \uc911\uc694\ud558\uc9c0\ub9cc, \uc218\uc5b5 \uba85\uc758 \uc0ac\uc6a9\uc790\uc5d0\uac8c \uc11c\ube44\uc2a4\ub97c \uc81c\uacf5\ud558\uae30 \uc704\ud55c \uc778\ud504\ub77c \ubc0f \ucef4\ud4e8\ud305 \ube44\uc6a9\uc740 \ucc9c\ubb38\ud559\uc801\uc785\ub2c8\ub2e4. \ub530\ub77c\uc11c \ube44\uc6a9 \ud6a8\uc728\uc801\uc778 \ubaa8\ub378 \uc124\uacc4\uc640 \uc6b4\uc601 \uc804\ub7b5\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc774\ucee4\uba38\uc2a4 \uc0c1\ud488 \ucd94\ucc9c:<\/strong> \uc628\ub77c\uc778 \uc1fc\ud551\ubab0\uc5d0\uc11c \uc0ac\uc6a9\uc790\uc5d0\uac8c \ub9de\ub294 \uc0c1\ud488\uc744 \ucd94\ucc9c\ud558\ub294 \uc2dc\uc2a4\ud15c \uc5ed\uc2dc \ub9c8\ucc2c\uac00\uc9c0\uc785\ub2c8\ub2e4. \uc218\ubc31\ub9cc \uac1c\uc758 \uc0c1\ud488\uacfc \uc218\ucc9c\ub9cc \uba85\uc758 \uc0ac\uc6a9\uc790\ub97c \ub300\uc0c1\uc73c\ub85c \uc2e4\uc2dc\uac04 \ucd94\ucc9c\uc744 \ud558\ub824\uba74 \ub9c9\ub300\ud55c \ucef4\ud4e8\ud305 \uc790\uc6d0\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 1% \ud5a5\uc0c1\ub418\ub294 \uac83\ubcf4\ub2e4, \uc6b4\uc601 \ube44\uc6a9\uc744 10% \uc808\uac10\ud558\ub294 \uac83\uc774 \ud6e8\uc52c \ub354 \ud070 \ube44\uc988\ub2c8\uc2a4 \uac00\uce58\ub97c \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\ub300\uaddc\ubaa8 \uc0ac\uc6a9\uc790 \ub300\uc0c1 \uc11c\ube44\uc2a4\uc5d0\uc11c\ub294 \uc870\uae08 \ub354 \ub0ae\uc740 \uc131\ub2a5\uc758 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub354\ub77c\ub3c4, \uc6b4\uc601\ube44\ub97c \uc808\uac10\ud558\uc5ec \ub354 \ub9ce\uc740 \uc0ac\uc6a9\uc790\uc5d0\uac8c \uc548\uc815\uc801\uc73c\ub85c \uc11c\ube44\uc2a4\ub97c \uc81c\uacf5\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc774\ub294 \uace7 \uac00\uaca9 \uacbd\uc7c1\ub825 \ud655\ubcf4\uc640 \uc9c1\uacb0\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h4>3. \uc2e4\uc2dc\uac04 \uc751\ub2f5 \uc18d\ub3c4\uac00 \uc911\uc694\ud55c \uc560\ud50c\ub9ac\ucf00\uc774\uc158<\/h4>\n<p>\uc2e4\uc2dc\uac04\uc73c\ub85c \uc989\uac01\uc801\uc778 \uc751\ub2f5\uc774 \ud544\uc694\ud55c \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c\ub294 \ubaa8\ub378\uc758 \ubcf5\uc7a1\uc131\uc73c\ub85c \uc778\ud55c \uc751\ub2f5 \uc9c0\uc5f0\uc774 \uc11c\ube44\uc2a4 \ud488\uc9c8\uc744 \uc800\ud558\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28:<\/strong> \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\ub294 \uc8fc\ubcc0 \ud658\uacbd\uc744 \uc2e4\uc2dc\uac04\uc73c\ub85c \uc778\uc2dd\ud558\uace0 \uc989\uac01\uc801\uc73c\ub85c \ud310\ub2e8\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub54c \uc0ac\uc6a9\ub418\ub294 AI \ubaa8\ub378\uc774 \ub108\ubb34 \ubcf5\uc7a1\ud558\uac70\ub098 \uc5f0\uc0b0\ub7c9\uc774 \ub9ce\uc73c\uba74, \uc758\uc0ac \uacb0\uc815\uc5d0 \uc9c0\uc5f0\uc774 \ubc1c\uc0dd\ud558\uc5ec \uce58\uba85\uc801\uc778 \uc0ac\uace0\ub85c \uc774\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc131\ub2a5\uacfc \uc751\ub2f5 \uc18d\ub3c4\ub97c \ub3d9\uc2dc\uc5d0 \ub9cc\uc871\uc2dc\ud0a4\uba74\uc11c\ub3c4, \uc81c\ud55c\ub41c \ucef4\ud4e8\ud305 \ud658\uacbd\uc5d0\uc11c \ud6a8\uc728\uc801\uc73c\ub85c \uc791\ub3d9\ud558\ub294 \ubaa8\ub378\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc2e4\uc2dc\uac04 \uac8c\uc784 AI:<\/strong> \uac8c\uc784 \ub0b4 NPC(Non-Player Character)\uc758 \ud589\ub3d9\uc744 \uc81c\uc5b4\ud558\ub294 AI \uc5ed\uc2dc \uc2e4\uc2dc\uac04 \uc751\ub2f5\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ubcf5\uc7a1\ud558\uace0 \uace0\uc131\ub2a5\uc758 AI \ubaa8\ub378\uc740 \uac8c\uc784\uc758 \ud504\ub808\uc784 \uc18d\ub3c4\ub97c \ub5a8\uc5b4\ub728\ub824 \uc0ac\uc6a9\uc790 \uacbd\ud5d8\uc744 \ud574\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \uac8c\uc784 \uc5d4\uc9c4\uacfc\uc758 \ud638\ud658\uc131, \ube60\ub978 \uc751\ub2f5 \uc18d\ub3c4, \uadf8\ub9ac\uace0 \uc801\uc808\ud55c \uc218\uc900\uc758 \uc9c0\ub2a5\uc744 \uac16\ucd98 \ubaa8\ub378\uc744 \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\uc774\ub7ec\ud55c \uacbd\uc6b0, \ucd5c\uace0\uc758 \uc131\ub2a5\uc744 \uac00\uc9c4 \ubaa8\ub378\uc774\ub77c\ub3c4 \uc2e4\uc2dc\uac04 \uc751\ub2f5\uc774 \ubd88\uac00\ub2a5\ud558\ub2e4\uba74 \ubb34\uc6a9\uc9c0\ubb3c\uc785\ub2c8\ub2e4. \uc624\ud788\ub824 \uc57d\uac04\uc758 \uc131\ub2a5 \ud76c\uc0dd\uc744 \uac10\uc218\ud558\ub354\ub77c\ub3c4, \ube60\ub974\uace0 \uc548\uc815\uc801\uc778 \uc751\ub2f5 \uc18d\ub3c4\ub97c \ubcf4\uc7a5\ud558\ub294 \ubaa8\ub378\uc774 \ub354 \uac00\uce58 \uc788\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h4>4. \uc790\uc6d0 \uc81c\uc57d\uc801\uc778 \ud658\uacbd\uc5d0\uc11c\uc758 \ud65c\uc6a9<\/h4>\n<p>\ubaa8\ubc14\uc77c \uae30\uae30, IoT \uc7a5\uce58, \ub610\ub294 \ud2b9\uc815 \ud558\ub4dc\uc6e8\uc5b4 \ud658\uacbd\uacfc \uac19\uc774 \ucef4\ud4e8\ud305 \uc790\uc6d0\uc774 \uc81c\ud55c\uc801\uc778 \ud658\uacbd\uc5d0\uc11c\ub294 \ubaa8\ub378\uc758 \ud06c\uae30\uc640 \uc5f0\uc0b0\ub7c9\uc774 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ubaa8\ubc14\uc77c \uc571 \ub0b4 AI \uae30\ub2a5:<\/strong> \uc2a4\ub9c8\ud2b8\ud3f0 \uc571\uc5d0\uc11c \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc74c\uc131 \uc778\uc2dd \ub4f1\uc758 AI \uae30\ub2a5\uc744 \uad6c\ud604\ud560 \ub54c, \ud074\ub77c\uc6b0\ub4dc \uc11c\ubc84\uc5d0 \uc758\uc874\ud558\uc9c0 \uc54a\uace0 \uae30\uae30 \uc790\uccb4\uc5d0\uc11c \ucc98\ub9ac\ud574\uc57c \ud558\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \uc774 \uacbd\uc6b0, \uae30\uae30\uc758 \uc131\ub2a5 \ud55c\uacc4\uc640 \ubc30\ud130\ub9ac \uc18c\ubaa8\ub97c \uace0\ub824\ud558\uc5ec \uac00\ubccd\uace0 \ud6a8\uc728\uc801\uc778 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc784\ubca0\ub514\ub4dc \uc2dc\uc2a4\ud15c:<\/strong> \uc2a4\ub9c8\ud2b8 \uac00\uc804, \uc0b0\uc5c5\uc6a9 \uc13c\uc11c \ub4f1 \ud2b9\uc815 \uae30\ub2a5\uc744 \uc218\ud589\ud558\uae30 \uc704\ud574 \uc124\uacc4\ub41c \uc784\ubca0\ub514\ub4dc \uc2dc\uc2a4\ud15c\uc5d0\uc11c\ub294 \ub9e4\uc6b0 \uc81c\ud55c\ub41c \uc790\uc6d0\uc73c\ub85c AI \ubaa8\ub378\uc744 \uc2e4\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub7f4 \ub54c\ub294 \ubaa8\ub378\uc758 \ud06c\uae30\ub97c \ucd5c\uc18c\ud654\ud558\uace0, \uc800\uc804\ub825\uc73c\ub85c \uc791\ub3d9\ud558\ub294 \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\uc774\ub7ec\ud55c \ud658\uacbd\uc5d0\uc11c\ub294 \ucd5c\uc2e0 \ub300\uaddc\ubaa8 \uc5b8\uc5b4 \ubaa8\ub378(LLM)\ucc98\ub7fc \ubc29\ub300\ud55c \uc790\uc6d0\uc744 \uc694\uad6c\ud558\ub294 \ubaa8\ub378\uc740 \uc0ac\uc6a9\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4. \ub300\uc2e0, \uacbd\ub7c9\ud654\ub41c \ubaa8\ub378\uc774\ub098 \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ud2b9\ud654\ub41c \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\ub294 \uac83\uc774 \ud604\uc2e4\uc801\uc778 \ub300\uc548\uc785\ub2c8\ub2e4.<\/p>\n<h3>AI \ubaa8\ub378 \uc120\ud0dd \uc2dc \uace0\ub824\ud574\uc57c \ud560 \uae30\uc900\ub4e4<\/h3>\n<p>\uadf8\ub807\ub2e4\uba74 \uc774\uc81c AI \ubaa8\ub378\uc744 \uc120\ud0dd\ud560 \ub54c \uc5b4\ub5a4 \uae30\uc900\uc73c\ub85c \uc811\uadfc\ud574\uc57c \ud560\uae4c\uc694? \ub2e8\uc21c\ud788 &#8216;\uc131\ub2a5&#8217;\ub9cc \ubcf4\ub294 \uac83\uc774 \uc544\ub2c8\ub77c, \ub2e4\uc74c\uacfc \uac19\uc740 \uc694\uc18c\ub4e4\uc744 \uc885\ud569\uc801\uc73c\ub85c \uace0\ub824\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<h4>1. \uba85\ud655\ud55c \ubaa9\ud45c \uc124\uc815 \ubc0f \uc131\ub2a5 \uce21\uc815<\/h4>\n<p>\uac00\uc7a5 \uba3c\uc800, AI \ubaa8\ub378\uc744 \ud1b5\ud574 \ub2ec\uc131\ud558\uace0\uc790 \ud558\ub294 \uad6c\uccb4\uc801\uc778 \ubaa9\ud45c\ub97c \uba85\ud655\ud788 \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ubb34\uc5c7\uc744 \ud574\uacb0\ud558\uace0 \uc2f6\uc740\uac00?<\/strong> (\uc608: \uace0\uac1d \ubb38\uc758 \uc751\ub300 \uc2dc\uac04 \ub2e8\ucd95, \uc0c1\ud488 \ucd94\ucc9c \uc815\ud655\ub3c4 \ud5a5\uc0c1, \ud2b9\uc815 \ubb38\uc11c \uc815\ubcf4 \uc790\ub3d9 \ucd94\ucd9c \ub4f1)<\/p>\n<\/li>\n<li>\n<p><strong>\uc131\uacf5\uc758 \uae30\uc900\uc740 \ubb34\uc5c7\uc778\uac00?<\/strong> (\uc608: \uc751\ub300 \uc2dc\uac04 20% \ub2e8\ucd95, \ucd94\ucc9c \ud074\ub9ad\ub960 5% \uc99d\uac00, \ucd94\ucd9c \uc815\ud655\ub3c4 98% \uc774\uc0c1 \ub2ec\uc131 \ub4f1)<\/p>\n<\/li>\n<\/ul>\n<p>\ubaa9\ud45c\uac00 \uba85\ud655\ud574\uc57c \ud544\uc694\ud55c AI \ubaa8\ub378\uc758 \uc131\ub2a5 \uc218\uc900\uc744 \uac00\ub2a0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, 99%\uc758 \uc815\ud655\ub3c4\uac00 \ud544\uc694\ud55c \uc5c5\ubb34\uc640 90%\uc758 \uc815\ud655\ub3c4\ub85c\ub3c4 \ucda9\ubd84\ud55c \uc5c5\ubb34\ub294 \uc694\uad6c\ud558\ub294 \ubaa8\ub378\uc758 \ubcf5\uc7a1\uc131\uacfc \ube44\uc6a9\uc774 \ud06c\uac8c \ub2e4\ub985\ub2c8\ub2e4.<\/p>\n<h4>2. \uc6b4\uc601\ube44 \uc608\uce21 \ubc0f \ubd84\uc11d<\/h4>\n<p>AI \ubaa8\ub378\uc758 \uc131\ub2a5\ub9cc\ud07c\uc774\ub098 \uc911\uc694\ud55c \uac83\uc774 \ubc14\ub85c \uc6b4\uc601\ube44\uc785\ub2c8\ub2e4. \ubaa8\ub378 \uc120\ud0dd \ub2e8\uacc4\uc5d0\uc11c\ubd80\ud130 \uc608\uc0c1\ub418\ub294 \uc6b4\uc601\ube44\ub97c \uaf3c\uaf3c\ud558\uac8c \ubd84\uc11d\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ud559\uc2b5 \ube44\uc6a9:<\/strong> \ubaa8\ub378 \ud559\uc2b5\uc5d0 \ud544\uc694\ud55c \ucef4\ud4e8\ud305 \uc790\uc6d0(GPU, CPU \ub4f1)\uacfc \uc2dc\uac04, \uadf8\ub9ac\uace0 \ub370\uc774\ud130 \uc900\ube44 \ube44\uc6a9\uc744 \uace0\ub824\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ucd94\ub860(Inference) \ube44\uc6a9:<\/strong> \ubaa8\ub378\uc774 \uc2e4\uc81c \uc0ac\uc6a9\ub420 \ub54c \ubc1c\uc0dd\ud558\ub294 \ube44\uc6a9\uc785\ub2c8\ub2e4. \uc0ac\uc6a9\ub7c9, \ud544\uc694\ud55c \ucef4\ud4e8\ud305 \uc131\ub2a5, \ud074\ub77c\uc6b0\ub4dc \uc11c\ube44\uc2a4 \uc694\uae08 \ub4f1\uc744 \uacc4\uc0b0\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc720\uc9c0\ubcf4\uc218 \ubc0f \uc5c5\ub370\uc774\ud2b8 \ube44\uc6a9:<\/strong> \ubaa8\ub378\uc744 \uc9c0\uc18d\uc801\uc73c\ub85c \uad00\ub9ac\ud558\uace0 \uac1c\uc120\ud558\ub294 \ub370 \ub4dc\ub294 \uc778\ub825 \ubc0f \uc778\ud504\ub77c \ube44\uc6a9\ub3c4 \ud3ec\ud568\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\uc774\ub7ec\ud55c \uc6b4\uc601\ube44 \ubd84\uc11d\uc744 \ud1b5\ud574, \ub2e8\uc21c\ud788 \ucd08\uae30 \uac1c\ubc1c \ube44\uc6a9\uc774 \uc800\ub834\ud55c \ubaa8\ub378\ubcf4\ub2e4\ub294 \uc7a5\uae30\uc801\uc73c\ub85c \ubd24\uc744 \ub54c \uacbd\uc81c\uc801\uc778 \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \ud604\uba85\ud569\ub2c8\ub2e4.<\/p>\n<h4>3. \ubaa8\ub378\uc758 \ubcf5\uc7a1\uc131\uacfc \uc790\uc6d0 \uc694\uad6c\ub7c9<\/h4>\n<p>\ubaa8\ub378\uc758 \ubcf5\uc7a1\uc131\uc740 \uace7 \uc6b4\uc601\ube44\uc640 \uc9c1\uacb0\ub429\ub2c8\ub2e4. \ubaa8\ub378\uc774 \ubcf5\uc7a1\ud560\uc218\ub85d \ub354 \ub9ce\uc740 \ucef4\ud4e8\ud305 \uc790\uc6d0\uc744 \uc694\uad6c\ud558\uba70, \uc774\ub294 \uace7 \ube44\uc6a9 \uc0c1\uc2b9\uc73c\ub85c \uc774\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ubaa8\ub378 \ud06c\uae30:<\/strong> \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130 \uc218\uac00 \ub9ce\uc744\uc218\ub85d \ud06c\uae30\uac00 \ucee4\uc9c0\uace0, \ub354 \ub9ce\uc740 \uba54\ubaa8\ub9ac\uc640 \uc5f0\uc0b0 \ub2a5\ub825\uc744 \ud544\uc694\ub85c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc5f0\uc0b0\ub7c9:<\/strong> \ubaa8\ub378\uc774 \ucd94\ub860 \uacfc\uc815\uc5d0\uc11c \uc218\ud589\ud574\uc57c \ud558\ub294 \uacc4\uc0b0\ub7c9\uc774 \ub9ce\uc744\uc218\ub85d \ucc98\ub9ac \uc2dc\uac04\uc774 \uc624\ub798 \uac78\ub9ac\uace0 \ub354 \ub9ce\uc740 \uc5d0\ub108\uc9c0\ub97c \uc18c\ubaa8\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\ub530\ub77c\uc11c \ubaa9\ud45c \uc131\ub2a5\uc744 \ub2ec\uc131\ud558\uba74\uc11c\ub3c4 \ucd5c\ub300\ud55c \ub2e8\uc21c\ud558\uace0 \ud6a8\uc728\uc801\uc778 \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ub54c\ub85c\ub294 \uc57d\uac04\uc758 \uc131\ub2a5 \uc800\ud558\ub97c \uac10\uc218\ud558\ub354\ub77c\ub3c4, \ud6e8\uc52c \ud6a8\uc728\uc801\uc778 \ubaa8\ub378\uc774 \ub354 \ub098\uc740 \uc120\ud0dd\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h4>4. \ud655\uc7a5\uc131 \ubc0f \uc720\uc5f0\uc131<\/h4>\n<p>AI \ubaa8\ub378\uc740 \ud55c\ubc88 \ub3c4\uc785\ud558\uace0 \ub05d\ub098\ub294 \uac83\uc774 \uc544\ub2c8\ub77c, \ube44\uc988\ub2c8\uc2a4 \ud658\uacbd \ubcc0\ud654\uc5d0 \ub530\ub77c \ud655\uc7a5\ub418\uac70\ub098 \uc218\uc815\ub420 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ub370\uc774\ud130 \uc99d\uac00\uc5d0 \ub300\ud55c \ub300\uc751:<\/strong> \ud5a5\ud6c4 \ub370\uc774\ud130 \uc591\uc774 \ub298\uc5b4\ub098\ub354\ub77c\ub3c4 \uc131\ub2a5 \uc800\ud558 \uc5c6\uc774 \uc11c\ube44\uc2a4\ub97c \uc720\uc9c0\ud560 \uc218 \uc788\ub294\uc9c0 \uace0\ub824\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc0c8\ub85c\uc6b4 \uae30\ub2a5 \ucd94\uac00:<\/strong> \ube44\uc988\ub2c8\uc2a4 \uc694\uad6c\uc0ac\ud56d \ubcc0\ud654\uc5d0 \ub530\ub77c \ubaa8\ub378\uc5d0 \uc0c8\ub85c\uc6b4 \uae30\ub2a5\uc744 \ucd94\uac00\ud558\uac70\ub098 \uae30\uc874 \uae30\ub2a5\uc744 \uc218\uc815\ud558\uae30 \uc6a9\uc774\ud55c \uad6c\uc870\uc778\uc9c0 \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\uc720\uc5f0\ud558\uace0 \ud655\uc7a5 \uac00\ub2a5\ud55c \ubaa8\ub378\uc740 \uc7a5\uae30\uc801\uc778 \uad00\uc810\uc5d0\uc11c \uc720\uc9c0\ubcf4\uc218 \ube44\uc6a9\uc744 \uc808\uac10\ud558\uace0 \ube44\uc988\ub2c8\uc2a4 \ubbfc\ucca9\uc131\uc744 \ub192\uc774\ub294 \ub370 \uae30\uc5ec\ud569\ub2c8\ub2e4.<\/p>\n<h4>5. \ub370\uc774\ud130 \ud504\ub77c\uc774\ubc84\uc2dc \ubc0f \ubcf4\uc548<\/h4>\n<p>AI \ubaa8\ub378\uc744 \uc6b4\uc601\ud560 \ub54c\ub294 \ubbfc\uac10\ud55c \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc73c\ubbc0\ub85c, \ub370\uc774\ud130 \ud504\ub77c\uc774\ubc84\uc2dc\uc640 \ubcf4\uc548\uc740 \ub9e4\uc6b0 \uc911\uc694\ud55c \uace0\ub824 \uc0ac\ud56d\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ub370\uc774\ud130 \ucc98\ub9ac \ubc29\uc2dd:<\/strong> \ubaa8\ub378\uc774 \ub370\uc774\ud130\ub97c \uc5b4\ub5bb\uac8c \uc218\uc9d1, \uc800\uc7a5, \ucc98\ub9ac\ud558\ub294\uc9c0 \uc774\ud574\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ubcf4\uc548 \uc870\uce58:<\/strong> \ub370\uc774\ud130 \uc720\ucd9c\uc774\ub098 \uc545\uc758\uc801\uc778 \uc811\uadfc\uc744 \ubc29\uc9c0\ud558\uae30 \uc704\ud55c \ubcf4\uc548 \uc870\uce58\uac00 \uc5bc\ub9c8\ub098 \uc798 \uac16\ucdb0\uc838 \uc788\ub294\uc9c0 \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\ud2b9\ud788 \uac1c\uc778 \uc815\ubcf4\ub098 \uae30\uc5c5 \ube44\ubc00\uacfc \uad00\ub828\ub41c \ub370\uc774\ud130\ub97c \ub2e4\ub8ec\ub2e4\uba74, \ubcf4\uc548\uc774 \uac15\ub825\ud55c \ubaa8\ub378\uacfc \uc194\ub8e8\uc158\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n<h3>AI \ubaa8\ub378 \uc120\ud0dd, \ud604\uba85\ud55c \uc811\uadfc \ubc29\uc2dd<\/h3>\n<p>AI \ubaa8\ub378 \uc120\ud0dd\uc740 \ub354 \uc774\uc0c1 &#8216;\uc131\ub2a5&#8217;\uc774\ub77c\ub294 \ud558\ub098\uc758 \uc7a3\ub300\ub85c\ub9cc \ud3c9\uac00\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4. \uc774\uc81c\ub294 &#8216;\ube44\uc6a9 \ud6a8\uc728\uc131&#8217;\uc774\ub77c\ub294 \ud604\uc2e4\uc801\uc778 \uad00\uc810\uc744 \ubc18\ub4dc\uc2dc \ud568\uaed8 \uace0\ub824\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uc791\uac8c \uc2dc\uc791\ud558\uace0 \uc810\uc9c4\uc801\uc73c\ub85c \ud655\uc7a5:<\/strong> \ucc98\uc74c\ubd80\ud130 \uac70\ub300\ud558\uace0 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \ub3c4\uc785\ud558\uae30\ubcf4\ub2e4\ub294, \uc791\uace0 \ud6a8\uc728\uc801\uc778 \ubaa8\ub378\ub85c \uc2dc\uc791\ud558\uc5ec \uc2e4\uc81c \uc6b4\uc601 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc810\uc9c4\uc801\uc73c\ub85c \uac1c\uc120\ud574 \ub098\uac00\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc624\ud508\uc18c\uc2a4 \ubaa8\ub378 \ubc0f \uc0ac\uc804 \ud559\uc2b5 \ubaa8\ub378 \ud65c\uc6a9:<\/strong> \ube44\uc6a9 \ud6a8\uc728\uc801\uc778 AI \ubaa8\ub378 \uad6c\ucd95\uc744 \uc704\ud574 \uc624\ud508\uc18c\uc2a4 \ubaa8\ub378\uc774\ub098 \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \uc801\uadf9\uc801\uc73c\ub85c \ud65c\uc6a9\ud558\ub294 \ubc29\uc548\uc744 \uace0\ub824\ud574 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\ub4e4\uc740 \uc774\ubbf8 \uc0c1\ub2f9\ud55c \uc131\ub2a5\uc744 \uac16\ucd94\uace0 \uc788\uc73c\uba70, \uc790\uccb4 \uac1c\ubc1c\uc5d0 \ube44\ud574 \uc2dc\uac04\uacfc \ube44\uc6a9\uc744 \uc808\uc57d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc804\ubb38\uac00\uc640\uc758 \uc0c1\ub2f4:<\/strong> AI \ubaa8\ub378 \uc120\ud0dd\uc740 \uc804\ubb38\uc801\uc778 \uc9c0\uc2dd\uc744 \uc694\uad6c\ud558\ub294 \ubd84\uc57c\uc785\ub2c8\ub2e4. \ub530\ub77c\uc11c AI \uc804\ubb38\uac00\ub098 \uad00\ub828 \ucee8\uc124\ud305 \uc5c5\uccb4\uc758 \ub3c4\uc6c0\uc744 \ubc1b\uc544, \ube44\uc988\ub2c8\uc2a4 \ubaa9\ud45c\uc640 \uc608\uc0b0\uc5d0 \ub9de\ub294 \ucd5c\uc801\uc758 \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \ud604\uba85\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>AI \uae30\uc220\uc740 \uacc4\uc18d\ud574\uc11c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \ubaa8\ub378 \uc120\ud0dd\uc758 \uae30\uc900 \ub610\ud55c \ubcc0\ud654\ud560 \uac83\uc785\ub2c8\ub2e4. \ud558\uc9c0\ub9cc &#8216;\ud6a8\uc728\uc131&#8217;\uacfc &#8216;\ube44\uc6a9 \ub300\ube44 \ud6a8\uacfc&#8217;\ub77c\ub294 \ud575\uc2ec \uc6d0\uce59\uc740 \uc55e\uc73c\ub85c\ub3c4 AI \ubaa8\ub378 \uc120\ud0dd\uc5d0 \uc788\uc5b4 \uc911\uc694\ud55c \ub098\uce68\ubc18\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<h3>\uacb0\ub860<\/h3>\n<p>AI \ubaa8\ub378 \uc120\ud0dd\uc758 \uae30\uc900\uc774 \uc131\ub2a5 \uc911\uc2ec\uc5d0\uc11c \uc6b4\uc601\ube44 \uc911\uc2ec\uc73c\ub85c \uc774\ub3d9\ud558\ub294 \uac83\uc740 \uc790\uc5f0\uc2a4\ub7ec\uc6b4 \ud604\uc0c1\uc785\ub2c8\ub2e4. \ud2b9\ud788 \ubc18\ubcf5\uc801\uc778 \uc5c5\ubb34 \uc790\ub3d9\ud654, \ub300\uaddc\ubaa8 \uc0ac\uc6a9\uc790 \ub300\uc0c1 \uc11c\ube44\uc2a4, \uc2e4\uc2dc\uac04 \uc751\ub2f5\uc774 \uc911\uc694\ud55c \uc560\ud50c\ub9ac\ucf00\uc774\uc158, \uadf8\ub9ac\uace0 \uc790\uc6d0 \uc81c\uc57d\uc801\uc778 \ud658\uacbd\uc5d0\uc11c\ub294 \uc6b4\uc601\ube44\uac00 \uc131\ub2a5\ub9cc\ud07c, \ud639\uc740 \uadf8 \uc774\uc0c1\uc73c\ub85c \uc911\uc694\ud55c \uace0\ub824 \uc0ac\ud56d\uc774 \ub429\ub2c8\ub2e4.<\/p>\n<p>AI \ubaa8\ub378\uc744 \uc120\ud0dd\ud560 \ub54c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc810\uc744 \uae30\uc5b5\ud558\uc138\uc694.<\/p>\n<ol>\n<li>\n<p><strong>\uba85\ud655\ud55c \ubaa9\ud45c \uc124\uc815:<\/strong> \ud574\uacb0\ud558\uace0\uc790 \ud558\ub294 \ubb38\uc81c\uc640 \uc131\uacf5 \uae30\uc900\uc744 \uad6c\uccb4\uc801\uc73c\ub85c \uc815\uc758\ud558\uc138\uc694.<\/p>\n<\/li>\n<li>\n<p><strong>\uc885\ud569\uc801\uc778 \ube44\uc6a9 \ubd84\uc11d:<\/strong> \uac1c\ubc1c \ube44\uc6a9\ubfd0\ub9cc \uc544\ub2c8\ub77c \uc7a5\uae30\uc801\uc778 \uc6b4\uc601, \uc720\uc9c0\ubcf4\uc218 \ube44\uc6a9\uae4c\uc9c0 \uaf3c\uaf3c\ud788 \uc608\uce21\ud558\uc138\uc694.<\/p>\n<\/li>\n<li>\n<p><strong>\ud6a8\uc728\uc801\uc778 \ubaa8\ub378 \uc120\ud0dd:<\/strong> \ubaa9\ud45c \uc131\ub2a5\uc744 \ub2ec\uc131\ud558\uba74\uc11c\ub3c4, \ucd5c\uc18c\ud55c\uc758 \uc790\uc6d0\uc744 \uc0ac\uc6a9\ud558\ub294 \ud6a8\uc728\uc801\uc778 \ubaa8\ub378\uc744 \uc6b0\uc120\uc801\uc73c\ub85c \uace0\ub824\ud558\uc138\uc694.<\/p>\n<\/li>\n<li>\n<p><strong>\uc810\uc9c4\uc801 \uc811\uadfc:<\/strong> \uc791\uac8c \uc2dc\uc791\ud558\uc5ec \uc2e4\uc81c \uc6b4\uc601 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ubaa8\ub378\uc744 \uac1c\uc120\ud558\uace0 \ud655\uc7a5\ud574 \ub098\uac00\uc138\uc694.<\/p>\n<\/li>\n<\/ol>\n<p>\uc774\ub7ec\ud55c \uae30\uc900\ub4e4\uc744 \ubc14\ud0d5\uc73c\ub85c \ud604\uba85\ud558\uac8c AI \ubaa8\ub378\uc744 \uc120\ud0dd\ud55c\ub2e4\uba74, \uae30\uc220\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \ube44\uc988\ub2c8\uc2a4\uc758 \uc131\uacf5\uc744 \ub354\uc6b1 \ud655\uc2e4\ud558\uac8c \uc774\ub04c\uc5b4\uac08 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<div class=\"content-links-section\">\n<p><strong>INTERNAL_LINKS:<\/strong> (\uc720\uc0ac\ud55c \uac8c\uc2dc\uae00 \uc785\ub825)<\/p>\n<p><strong>EXTERNAL_LINKS:<\/strong> <a href=\"https:\/\/example.com\/ai-model-selection-guide\" target=\"_blank\" rel=\"noopener noreferrer\">AI \ubaa8\ub378 \uc120\ud0dd \uac00\uc774\ub4dc\ub77c\uc778<\/a>, <a href=\"https:\/\/example.com\/cloud-ai-cost-saving\" target=\"_blank\" rel=\"noopener noreferrer\">\ud074\ub77c\uc6b0\ub4dc AI \ube44\uc6a9 \uc808\uac10 \ubc29\uc548<\/a>, <a href=\"https:\/\/example.com\/efficient-ai-model-design\" target=\"_blank\" rel=\"noopener noreferrer\">\ud6a8\uc728\uc801\uc778 AI \ubaa8\ub378 \uc124\uacc4<\/a><\/p>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Choosing AI Models: From a Performance-First Past to a Cost-Efficiency Present<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In the past, when selecting an AI model, <strong>performance<\/strong> was everything. The model that was more accurate, faster, and smarter was considered the best\u2014much like choosing a car based primarily on top speed or acceleration. But as AI technology has matured and become deeply embedded in everyday life, the criteria for choosing AI models are gradually changing. In particular, the practical issue of <strong>operating cost<\/strong> has become increasingly important.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why Are the Criteria for Choosing AI Models Changing?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Developing and deploying AI models costs more than many people expect. The expense is not limited to building the model itself; there are also ongoing costs involved in maintaining and running it.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Growing Data Volume and Complexity<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI models generally perform better when trained on larger amounts of data. But as the volume of data increases, so do the costs of storing and managing it. In addition, as models become more complex, they require greater computing resources, which directly leads to higher operating costs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Need for Continuous Operation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Many AI services must operate around the clock, 24 hours a day, 365 days a year. Services such as chatbots and recommendation systems need to remain accessible whenever users need them, which means that server operation and maintenance costs continue without interruption.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Cloud Computing Costs<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI models are often trained and run using cloud services. Since cloud pricing is typically based on usage, costs rise as model usage increases. In particular, complex computation or large-scale data processing can generate unexpectedly high expenses.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ongoing Updates and Improvements<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">An AI model is not something that is built once and then left alone. It must be continuously updated and improved in response to market changes, new data, and user feedback. This process also consumes computing resources and human labor, which adds further cost.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In this way, <strong>running an AI model well<\/strong> has become just as important as building one. Choosing a model based on performance alone can now result in unexpected operating cost burdens later on.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">When Does Operating Cost Matter More Than Performance?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">So in what situations does operating cost become more important than AI model performance? Several representative cases illustrate this clearly.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. Automating Repetitive and Routine Tasks<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">When deploying AI solutions for repetitive, everyday work, operating cost becomes a critical consideration. This includes tasks such as handling simple customer inquiries through a chatbot or extracting specific information from documents.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Chatbots<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">If hundreds or thousands of simple inquiries are received each day, the operating cost of the chatbot handling them can become a major part of the total system expense. In such a case, it may be more efficient to choose a model that can provide a sufficiently consistent level of response quality at a reasonable cost, rather than using a very expensive model with extremely advanced natural language abilities.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Information Extraction<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The same applies when building an AI model to extract specific data from documents in a fixed format. This type of task is relatively structured and usually does not require extreme creativity or complex reasoning. Rather than using the newest and highest-performing model, it may be more economical to choose a model that is optimized for the specific task and cheaper to run.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In such cases, the practical business difference between a model with 99% accuracy and one with 95% accuracy may be small. But if the operating cost differs by two or three times, choosing the lower-cost model is clearly the more rational decision.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Services for Large User Bases<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In services where huge numbers of users connect at the same time, operating cost can become a decisive factor for sustainability.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Social Media Feed Recommendations<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Platforms such as Facebook and Instagram serve hundreds of millions of users in real time. AI models must constantly operate to recommend personalized feeds. Performance matters, but the infrastructure and computing costs required to serve that scale are enormous. In this context, cost-efficient model design and operational strategy are essential.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">E-Commerce Product Recommendations<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The same is true for systems that recommend products to users in online shopping platforms. Real-time recommendations for millions of products and tens of millions of users require tremendous computing resources. In this environment, a 1% gain in model performance may matter less than a 10% reduction in operating cost, which could provide much greater business value.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For large-scale services, it is often more important to provide stable service to more users at lower cost than to squeeze out a small gain in model performance. This can directly translate into stronger price competitiveness.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Applications Where Real-Time Response Matters<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In applications requiring immediate, real-time responses, delays caused by model complexity can reduce service quality.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Autonomous Vehicles<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Self-driving cars must perceive their surroundings and make decisions in real time. If the AI model is too complex or computationally heavy, delays in decision-making could lead to critical accidents. In this case, the model must balance performance with response speed while operating efficiently within a constrained computing environment.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Real-Time Game AI<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">AI that controls non-player characters (NPCs) in games also depends heavily on immediate responses. A highly complex, high-performance model may reduce the game\u2019s frame rate and harm user experience. In such cases, the right choice is a model that works well with the game engine, responds quickly, and provides an appropriate level of intelligence.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In these scenarios, even the most capable model is useless if it cannot respond in time. A slightly less powerful model that guarantees fast and stable response may be far more valuable.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Deployment in Resource-Constrained Environments<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In environments where computing resources are limited\u2014such as mobile devices, IoT devices, or embedded systems\u2014the size of the model and the amount of computation it requires become especially important.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">AI Features in Mobile Apps<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">When implementing AI features such as image recognition or speech recognition in smartphone apps, it is often preferable to process tasks on the device itself rather than relying on cloud servers. In such cases, lightweight and efficient models are necessary, given device limitations and battery consumption.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Embedded Systems<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">In embedded systems such as smart appliances or industrial sensors, AI must run within very limited resources. Under these conditions, it is essential to choose models that are compact and energy-efficient.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In these environments, models such as the latest large language models (LLMs), which require vast resources, are often unrealistic. Lightweight or task-specific models are the practical alternative.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What Should Be Considered When Choosing an AI Model?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Selecting an AI model today requires more than simply comparing performance. The following factors should be considered together.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. Clear Goal Setting and Performance Measurement<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">First, the specific goal to be achieved through the AI model must be clearly defined.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What problem is the model intended to solve?<br>(For example: reducing customer response time, improving recommendation accuracy, automatically extracting information from certain documents)<\/li>\n\n\n\n<li>What counts as success?<br>(For example: reducing response time by 20%, increasing recommendation click-through rate by 5%, achieving information extraction accuracy above 98%)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Only when the goal is clearly defined can the necessary level of model performance be judged accurately. Some tasks may require 99% accuracy, while others may work well enough at 90%. The required model complexity and cost may differ greatly between the two.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Forecasting and Analyzing Operating Cost<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Operating cost is now just as important as model performance. At the selection stage, expected operating costs should be carefully analyzed.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Training cost:<\/strong> computing resources such as GPUs and CPUs, training time, and data preparation cost<\/li>\n\n\n\n<li><strong>Inference cost:<\/strong> the cost incurred during real-world use, based on usage volume, required computing performance, and cloud service fees<\/li>\n\n\n\n<li><strong>Maintenance and update cost:<\/strong> labor and infrastructure costs needed for continuous management and improvement<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This analysis makes it possible to choose not simply the cheapest model to develop at the outset, but the most economical model over the long term.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Model Complexity and Resource Requirements<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Model complexity is directly tied to operating cost. The more complex a model is, the more computing resources it requires, which drives costs upward.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Model size:<\/strong> more parameters mean a larger model, greater memory usage, and higher computational demand<\/li>\n\n\n\n<li><strong>Computation load:<\/strong> the more calculations required during inference, the longer processing takes and the more energy it consumes<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">It is therefore important to choose the simplest and most efficient model capable of meeting the target performance. In many cases, a slightly lower-performing but far more efficient model may be the better choice.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Scalability and Flexibility<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">An AI model is not deployed once and forgotten. It often needs to expand or change as the business environment evolves.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Handling future data growth:<\/strong> can the model maintain service quality as data volume increases?<\/li>\n\n\n\n<li><strong>Adding new functions:<\/strong> is the structure flexible enough to allow new features or modifications when business needs change?<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">A model that is scalable and flexible can reduce maintenance costs over time and improve business agility.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">5. Data Privacy and Security<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Since AI models often handle sensitive data, privacy and security are extremely important.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>How data is processed:<\/strong> it is necessary to understand how the model collects, stores, and processes data<\/li>\n\n\n\n<li><strong>Security measures:<\/strong> it is important to verify how well the system protects against data leakage and malicious access<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">If the model handles personal information or corporate secrets, strong security must be considered essential in model selection.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A Smarter Approach to AI Model Selection<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Choosing an AI model can no longer be done using performance alone as the standard. It now requires a realistic view that includes <strong>cost efficiency<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Start Small and Expand Gradually<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Rather than adopting a huge and complex model from the start, it is often better to begin with a smaller, more efficient model and improve it gradually based on actual operational data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Use Open-Source and Pretrained Models<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">When building cost-efficient AI systems, it is worth actively considering open-source models or pretrained models. These often already provide substantial performance and can save both time and money compared with full in-house development.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Consult Experts<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI model selection is a field that requires specialized knowledge. It is often wise to seek help from AI professionals or consulting firms in order to choose the most suitable model for the organization\u2019s goals and budget.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI technology will continue to evolve, and the criteria for model selection will continue to change. But the core principles of <strong>efficiency<\/strong> and <strong>cost-effectiveness<\/strong> are likely to remain essential guides in choosing AI models.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The shift in AI model selection from performance-centered thinking to operation-cost-centered thinking is a natural development. In particular, in areas such as repetitive task automation, large-scale user services, applications requiring real-time responses, and resource-constrained environments, operating cost can become just as important as\u2014or even more important than\u2014performance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When selecting an AI model, keep the following principles in mind:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Set clear goals:<\/strong> define the problem to be solved and the criteria for success in concrete terms.<\/li>\n\n\n\n<li><strong>Analyze costs comprehensively:<\/strong> forecast not only development costs but also long-term operating and maintenance costs.<\/li>\n\n\n\n<li><strong>Choose efficient models:<\/strong> prioritize models that achieve the desired level of performance while using the minimum necessary resources.<\/li>\n\n\n\n<li><strong>Take a gradual approach:<\/strong> start small, then improve and scale the model based on real operational data.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">A company that selects AI models wisely based on these principles will be better positioned to turn technological progress into real business success.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI \ubaa8\ub378 \uc120\ud0dd\uc758 \uae30\uc900\uc774 \uc131\ub2a5\uc5d0\uc11c \uc6b4\uc601\ube44\ub85c \uc62e\uaca8\uac00\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ud2b9\uc815 \uc0c1\ud669\uc5d0\uc11c\ub294 \uc6b4\uc601\ube44\uac00 \uc131\ub2a5\ubcf4\ub2e4 \ud6e8\uc52c \uc911\uc694\ud55c \uc694\uc18c\uac00 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uc65c \uc774\ub7f0 \ubcc0\ud654\uac00 \uc77c\uc5b4\ub098\uace0 \uc788\ub294\uc9c0, \uadf8\ub9ac\uace0 \uc5b8\uc81c \uc6b4\uc601\ube44\uac00 \uc911\uc694\ud574\uc9c0\ub294\uc9c0, \uc5b4\ub5a4 \uae30\uc900\uc73c\ub85c AI \ubaa8\ub378\uc744 \uc120\ud0dd\ud574\uc57c \ud560\uc9c0 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[4],"tags":[261,77,260,264,171,168,48,172,86,90,82,263,262,173,55,35,32,170,169],"class_list":["post-71","post","type-post","status-publish","format-standard","hentry","category-ai","tag-ai-adoption","tag-ai-cost","tag-ai-model-selection","tag-ai-strategy","tag-ai-","tag-ai--","tag-cost-efficiency","tag-deep-learning","tag-machine-learning","tag-performance","tag-technology-selection","tag-173","tag-55","tag-35","tag-32","tag-170","tag-169"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/71","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=71"}],"version-history":[{"count":1,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/71\/revisions"}],"predecessor-version":[{"id":96,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/71\/revisions\/96"}],"wp:attachment":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=71"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=71"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=71"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}