{"id":118,"date":"2026-04-27T23:22:35","date_gmt":"2026-04-27T14:22:35","guid":{"rendered":"https:\/\/ai-cloud.kr\/?p=118"},"modified":"2026-04-27T23:22:37","modified_gmt":"2026-04-27T14:22:37","slug":"ai-%eb%aa%a8%eb%8d%b8%eb%b3%b4%eb%8b%a4-%ec%a4%91%ec%9a%94%ed%95%9c-ai-%ec%a4%80%eb%b9%84-%eb%8d%b0%ec%9d%b4%ed%84%b0-%ec%99%9c-%ec%a0%95%eb%a6%ac%eb%90%9c-%eb%8d%b0%ec%9d%b4%ed%84%b0%ea%b0%80","status":"publish","type":"post","link":"https:\/\/ai-cloud.kr\/?p=118","title":{"rendered":"AI \ubaa8\ub378\ubcf4\ub2e4 \uc911\uc694\ud55c AI \uc900\ube44 \ub370\uc774\ud130, \uc65c \uc815\ub9ac\ub41c \ub370\uc774\ud130\uac00 \ud575\uc2ec\uc77c\uae4c?(AI-Ready Data Matters More Than AI Models: Why Well-Organized Data Is the Real Key)"},"content":{"rendered":"<h2>AI \ubaa8\ub378 \uc131\ub2a5, &#8216;\uc88b\uc740 \ub370\uc774\ud130&#8217;\uac00 \uba3c\uc800\ub2e4?<\/h2>\n<p>\uc778\uacf5\uc9c0\ub2a5(AI) \uae30\uc220\uc774 \uc6b0\ub9ac \uc0b6 \uacf3\uacf3\uc5d0 \uc2a4\uba70\ub4e4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc790\uc728\uc8fc\ud589\ucc28\ubd80\ud130 \uac1c\uc778 \ub9de\ucda4\ud615 \ucd94\ucc9c \uc11c\ube44\uc2a4\uae4c\uc9c0, AI\uc758 \ubc1c\uc804\uc740 \ub208\ubd80\uc2ed\ub2c8\ub2e4. \ub9ce\uc740 \uc0ac\ub78c\ub4e4\uc774 AI\uc758 \ud575\uc2ec\uc740 &#8216;\ub611\ub611\ud55c \uc54c\uace0\ub9ac\uc998&#8217;\uc774\ub098 &#8216;\ucd5c\ucca8\ub2e8 \ubaa8\ub378&#8217;\uc774\ub77c\uace0 \uc0dd\uac01\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc AI \uc804\ubb38\uac00\ub4e4\uc740 \uc785\uc744 \ubaa8\uc544 \ub9d0\ud569\ub2c8\ub2e4. &#8220;\uc544\ubb34\ub9ac \ud6cc\ub96d\ud55c \ubaa8\ub378\ub3c4 \ub370\uc774\ud130\uac00 \uc5c9\ub9dd\uc774\uba74 \uc81c\ub300\ub85c \uc791\ub3d9\ud558\uc9c0 \uc54a\ub294\ub2e4.&#8221; \uc989, AI \ubaa8\ub378\uc758 \uc131\uacf5\uc740 &#8216;\uc88b\uc740 \ubaa8\ub378&#8217; \uc774\uc804\uc5d0 &#8216;\uc798 \uc900\ube44\ub41c \ub370\uc774\ud130&#8217;\uc5d0 \ub2ec\ub824\uc788\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<h3>AI \uc900\ube44 \ub370\uc774\ud130\ub780 \ubb34\uc5c7\uc778\uac00?<\/h3>\n<p>AI \uc900\ube44 \ub370\uc774\ud130(AI-Ready Data)\ub294 AI \ubaa8\ub378 \ud559\uc2b5\uc5d0 \ubc14\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \uac00\uacf5\ub418\uace0 \uc815\uc81c\ub41c \ub370\uc774\ud130\ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4. \ub2e8\uc21c\ud788 \ub9ce\uc740 \uc591\uc758 \ub370\uc774\ud130\ub97c \ubaa8\uc73c\ub294 \uac83\uc744 \ub118\uc5b4, AI \ubaa8\ub378\uc774 \ud2b9\uc815 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ub370 \ud544\uc694\ud55c \uc815\ud655\ud558\uace0 \uad00\ub828\uc131 \ub192\uc740 \ud615\ud0dc\ub85c \ub370\uc774\ud130\ub97c \uc900\ube44\ud558\ub294 \uc804 \uacfc\uc815\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/p>\n<h3>\uc65c AI \uc900\ube44 \ub370\uc774\ud130\uac00 \ubaa8\ub378\ubcf4\ub2e4 \uc911\uc694\ud560\uae4c?<\/h3>\n<p>AI \ubaa8\ub378\uc740 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud559\uc2b5\ud558\uace0 \ud328\ud134\uc744 \uc778\uc2dd\ud569\ub2c8\ub2e4. \ub9cc\uc57d \ud559\uc2b5 \ub370\uc774\ud130\uc5d0 \uc624\ub958\uac00 \ub9ce\uac70\ub098, \ud3b8\ud5a5\ub418\uc5b4 \uc788\uac70\ub098, \uad00\ub828 \uc5c6\ub294 \uc815\ubcf4\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\ub2e4\uba74 AI \ubaa8\ub378\uc740 \uc798\ubabb\ub41c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uac8c \ub429\ub2c8\ub2e4. \uc774\ub294 \uace7 AI \uc11c\ube44\uc2a4\uc758 \uc131\ub2a5 \uc800\ud558, \uc608\uce21 \uc624\ub958, \uc2ec\uc9c0\uc5b4\ub294 \ucc28\ubcc4\uc801\uc778 \uacb0\uacfc\ub85c \uc774\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<p><strong>1. \ud559\uc2b5\uc758 \uc9c8, \ub370\uc774\ud130\uac00 \uacb0\uc815\ud55c\ub2e4:<\/strong><\/p>\n<p>AI \ubaa8\ub378\uc740 &#8216;\uc4f0\ub808\uae30\ub97c \ub123\uc73c\uba74 \uc4f0\ub808\uae30\uac00 \ub098\uc628\ub2e4(Garbage In, Garbage Out)&#8217;\ub294 \uc6d0\uce59\uc744 \ub530\ub985\ub2c8\ub2e4. \uc544\ubb34\ub9ac \uc815\uad50\ud55c \uc54c\uace0\ub9ac\uc998\uc774\ub77c\ub3c4 \ubd80\uc815\ud655\ud558\uac70\ub098 \ubd88\uc644\uc804\ud55c \ub370\uc774\ud130\ub85c\ub294 \uc62c\ubc14\ub978 \ud559\uc2b5\uc744 \uae30\ub300\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4. \ub9c8\uce58 \uc798\ubabb\ub41c \uc7ac\ub8cc\ub85c \uc544\ubb34\ub9ac \ud6cc\ub96d\ud55c \ub808\uc2dc\ud53c\ub97c \ub530\ub77c\ub3c4 \ub9db\uc788\ub294 \uc74c\uc2dd\uc744 \ub9cc\ub4e4 \uc218 \uc5c6\ub294 \uac83\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<p><strong>2. \ud3b8\ud5a5\uc131 \ubb38\uc81c \ud574\uacb0:<\/strong><\/p>\n<p>\ub370\uc774\ud130\uc5d0 \ud2b9\uc815 \uc9d1\ub2e8\uc5d0 \ub300\ud55c \ud3b8\ud5a5\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\ub2e4\uba74, AI \ubaa8\ub378 \uc5ed\uc2dc \uadf8 \ud3b8\ud5a5\uc744 \ud559\uc2b5\ud558\uc5ec \ucc28\ubcc4\uc801\uc778 \uacb0\uacfc\ub97c \ucd08\ub798\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud2b9\uc815 \uc778\uc885\uc774\ub098 \uc131\ubcc4\uc5d0 \ub300\ud55c \ub370\uc774\ud130\uac00 \ubd80\uc871\ud558\uac70\ub098 \ubd80\uc815\ud655\ud558\uac8c \ud3ec\ud568\ub41c \ucc44\ub85c \ud559\uc2b5\ub41c \ucc44\uc6a9 AI\ub294 \ud574\ub2f9 \uc9d1\ub2e8\uc5d0\uac8c \ubd88\ub9ac\ud55c \uacb0\uacfc\ub97c \ub0bc \uac00\ub2a5\uc131\uc774 \ub192\uc2b5\ub2c8\ub2e4. AI \uc900\ube44 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \uc774\ub7ec\ud55c \ud3b8\ud5a5\uc131\uc744 \uc778\uc9c0\ud558\uace0 \uc218\uc815\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n<p><strong>3. \uac1c\ubc1c \uc2dc\uac04 \ubc0f \ube44\uc6a9 \uc808\uac10:<\/strong><\/p>\n<p>\ucc98\uc74c\ubd80\ud130 \uc798 \uc900\ube44\ub41c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uba74, \ubaa8\ub378 \uac1c\ubc1c \uacfc\uc815\uc5d0\uc11c \ubc1c\uc0dd\ud558\ub294 \ub370\uc774\ud130 \uad00\ub828 \uc624\ub958\ub098 \ubb38\uc81c \ud574\uacb0\uc5d0 \ub4dc\ub294 \uc2dc\uac04\uacfc \ube44\uc6a9\uc744 \ud06c\uac8c \uc808\uac10\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \ub098\uc911\uc5d0 \uc218\uc815\ud558\ub294 \uac83\uc740 \ucc98\uc74c\ubd80\ud130 \uc81c\ub300\ub85c \uc900\ube44\ud558\ub294 \uac83\ubcf4\ub2e4 \ud6e8\uc52c \ub354 \ub9ce\uc740 \ub178\ub825\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n<p><strong>4. \ubaa8\ub378\uc758 \uc2e0\ub8b0\uc131 \ubc0f \uc77c\ubc18\ud654 \ub2a5\ub825 \ud5a5\uc0c1:<\/strong><\/p>\n<p>\uae68\ub057\ud558\uace0 \uc798 \uc815\uc81c\ub41c \ub370\uc774\ud130\ub85c \ud559\uc2b5\ub41c AI \ubaa8\ub378\uc740 \ub354 \ub192\uc740 \uc815\ud655\ub3c4\uc640 \uc2e0\ub8b0\uc131\uc744 \ubcf4\uc7a5\ud569\ub2c8\ub2e4. \ub610\ud55c, \ub2e4\uc591\ud55c \uc0c1\ud669\uacfc \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0\ub3c4 \uc798 \uc801\uc751\ud558\ub294 \uc77c\ubc18\ud654 \ub2a5\ub825\uc744 \uac16\ucd94\uac8c \ub418\uc5b4 \uc2e4\uc81c \uc11c\ube44\uc2a4\uc5d0\uc11c \ub354 \uc720\uc6a9\ud558\uac8c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>AI \uc900\ube44 \ub370\uc774\ud130 \uad6c\ucd95\uc744 \uc704\ud55c \ud575\uc2ec \ub2e8\uacc4<\/h2>\n<p>AI \uc900\ube44 \ub370\uc774\ud130\ub97c \ub9cc\ub4dc\ub294 \uacfc\uc815\uc740 \ub2e8\uc21c\ud788 \ub370\uc774\ud130\ub97c \ubaa8\uc73c\ub294 \uac83 \uc774\uc0c1\uc785\ub2c8\ub2e4. \uccb4\uacc4\uc801\uc778 \ub2e8\uacc4\ub97c \uac70\uccd0\uc57c\ub9cc AI \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>1. \ub370\uc774\ud130 \uc218\uc9d1 (Data Collection)<\/h3>\n<p>\uac00\uc7a5 \uba3c\uc800 AI \ubaa8\ub378\uc774 \ud574\uacb0\ud558\uace0\uc790 \ud558\ub294 \ubb38\uc81c\uc640 \uad00\ub828\ub41c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud574\uc57c \ud569\ub2c8\ub2e4. \ub370\uc774\ud130\uc758 \ucd9c\ucc98\ub294 \ub2e4\uc591\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ub0b4\ubd80 \ub370\uc774\ud130:<\/strong> \uae30\uc5c5\uc774 \ubcf4\uc720\ud55c \uace0\uac1d \uc815\ubcf4, \ud310\ub9e4 \uae30\ub85d, \ub85c\uadf8 \ub370\uc774\ud130 \ub4f1<\/p>\n<\/li>\n<li>\n<p><strong>\uc678\ubd80 \ub370\uc774\ud130:<\/strong> \uacf5\uac1c \ub370\uc774\ud130\uc14b, \uc6f9 \uc2a4\ud06c\ub798\ud551, \uc13c\uc11c \ub370\uc774\ud130, \uc18c\uc15c \ubbf8\ub514\uc5b4 \ub370\uc774\ud130 \ub4f1<\/p>\n<\/li>\n<li>\n<p><strong>\ud569\uc131 \ub370\uc774\ud130:<\/strong> \uc2e4\uc81c \ub370\uc774\ud130\uac00 \ubd80\uc871\ud558\uac70\ub098 \ubbfc\uac10\ud55c \uacbd\uc6b0, \uc2dc\ubbac\ub808\uc774\uc158\uc744 \ud1b5\ud574 \uc778\uacf5\uc801\uc73c\ub85c \uc0dd\uc131\ub41c \ub370\uc774\ud130<\/p>\n<\/li>\n<\/ul>\n<p><strong>\uc8fc\uc758\uc0ac\ud56d:<\/strong><\/p>\n<ul>\n<li>\n<p><strong>\ub370\uc774\ud130\uc758 \uad00\ub828\uc131:<\/strong> \uc218\uc9d1\ud558\ub294 \ub370\uc774\ud130\uac00 AI \ubaa8\ub378\uc758 \ubaa9\ud45c\uc640 \uc9c1\uc811\uc801\uc778 \uad00\ub828\uc774 \uc788\ub294\uc9c0 \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130\uc758 \ub2e4\uc591\uc131:<\/strong> \ud2b9\uc815 \uc0c1\ud669\uc774\ub098 \ub370\uc774\ud130\uc5d0 \uce58\uc6b0\uce58\uc9c0 \uc54a\ub3c4\ub85d \ub2e4\uc591\ud55c \uacbd\uc6b0\ub97c \ud3ec\uad04\ud558\ub294 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130\uc758 \ud569\ubc95\uc131 \ubc0f \uc724\ub9ac\uc131:<\/strong> \uac1c\uc778 \uc815\ubcf4 \ubcf4\ud638 \uaddc\uc815(GDPR, CCPA \ub4f1)\uc744 \uc900\uc218\ud558\uace0, \uc724\ub9ac\uc801\uc778 \ubb38\uc81c\ub97c \uc57c\uae30\ud560 \uc218 \uc788\ub294 \ub370\uc774\ud130\ub294 \uc218\uc9d1\ud558\uc9c0 \uc54a\ub3c4\ub85d \uc8fc\uc758\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h3>2. \ub370\uc774\ud130 \uc815\uc81c (Data Cleaning)<\/h3>\n<p>\uc218\uc9d1\ub41c \ub370\uc774\ud130\uc5d0\ub294 \uc624\ub958, \ub204\ub77d, \uc911\ubcf5, \uc774\uc0c1\uce58 \ub4f1 \ub2e4\uc591\ud55c \ubb38\uc81c\uac00 \ud3ec\ud568\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc815\uc81c\ub294 \uc774\ub7ec\ud55c \ubd88\uc21c\ubb3c\uc744 \uc81c\uac70\ud558\uace0 \ub370\uc774\ud130\uc758 \uc77c\uad00\uc131\uacfc \uc815\ud655\uc131\uc744 \ub192\uc774\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uacb0\uce21\uce58 \ucc98\ub9ac:<\/strong> \ub370\uc774\ud130\uac00 \ub204\ub77d\ub41c \ubd80\ubd84\uc744 \ucc44\uc6b0\uac70\ub098 \ud574\ub2f9 \ub370\uc774\ud130\ub97c \uc81c\uac70\ud569\ub2c8\ub2e4. (\uc608: \ud3c9\uade0\uac12, \uc911\uc559\uac12\uc73c\ub85c \ub300\uccb4, \uc774\uc804\/\ub2e4\uc74c \uac12\uc73c\ub85c \ubcf4\uac04, \ud589\/\uc5f4 \uc0ad\uc81c)<\/p>\n<\/li>\n<li>\n<p><strong>\uc774\uc0c1\uce58(Outlier) \ud0d0\uc9c0 \ubc0f \ucc98\ub9ac:<\/strong> \uc77c\ubc18\uc801\uc778 \ub370\uc774\ud130 \ubc94\uc704\uc5d0\uc11c \ubc97\uc5b4\ub098\ub294 \uac12\uc744 \ud0d0\uc9c0\ud558\uace0, \uc81c\uac70\ud558\uac70\ub098 \uc870\uc815\ud569\ub2c8\ub2e4. (\uc608: \ud1b5\uacc4\uc801 \uae30\ubc95, \uc2dc\uac01\ud654 \ud65c\uc6a9)<\/p>\n<\/li>\n<li>\n<p><strong>\uc911\ubcf5 \ub370\uc774\ud130 \uc81c\uac70:<\/strong> \ub3d9\uc77c\ud55c \ub370\uc774\ud130\uac00 \uc5ec\ub7ec \ubc88 \uae30\ub85d\ub41c \uacbd\uc6b0, \uc911\ubcf5\uc744 \uc81c\uac70\ud558\uc5ec \ub370\uc774\ud130\uc758 \uc815\ud655\uc131\uc744 \ub192\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \ud615\uc2dd \ud1b5\uc77c:<\/strong> \ub0a0\uc9dc, \uc2dc\uac04, \ub2e8\uc704 \ub4f1 \ub370\uc774\ud130 \ud615\uc2dd\uc744 \uc77c\uad00\ub418\uac8c \ub9de\ucdb0\uc90d\ub2c8\ub2e4. (\uc608: &#8216;2023-10-27&#8242;, &#8217;27\/10\/2023&#8217;, &#8216;Oct 27, 2023&#8217;\uc744 \ubaa8\ub450 &#8216;YYYY-MM-DD&#8217; \ud615\uc2dd\uc73c\ub85c \ud1b5\uc77c)<\/p>\n<\/li>\n<li>\n<p><strong>\uc624\ud0c0 \ubc0f \uc624\ub958 \uc218\uc815:<\/strong> \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\uc758 \uc624\ud0c0, \uc798\ubabb\ub41c \uae30\ud638 \ub4f1\uc744 \uc218\uc815\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p><strong>\uc608\uc2dc:<\/strong> \uace0\uac1d\uc758 \uc804\ud654\ubc88\ud638\uac00 &#8216;010-1234-5678&#8217;\uacfc &#8216;01012345678&#8217;\ub85c \ud63c\uc6a9\ub418\uc5b4 \uc788\ub2e4\uba74, \uc774\ub97c &#8216;010-1234-5678&#8217;\uacfc \uac19\uc740 \ud558\ub098\uc758 \ud45c\uc900 \ud615\uc2dd\uc73c\ub85c \ud1b5\uc77c\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<h3>3. \ub370\uc774\ud130 \ubcc0\ud658 (Data Transformation)<\/h3>\n<p>\uc815\uc81c\ub41c \ub370\uc774\ud130\ub97c AI \ubaa8\ub378\uc774 \uc774\ud574\ud558\uace0 \ud559\uc2b5\ud558\uae30 \uc88b\uc740 \ud615\ud0dc\ub85c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uc815\uaddc\ud654(Normalization) \ubc0f \ud45c\uc900\ud654(Standardization):<\/strong> \ub370\uc774\ud130\uc758 \uac12 \ubc94\uc704\ub97c \uc870\uc815\ud558\uc5ec \ubaa8\ub378 \ud559\uc2b5\uc758 \uc548\uc815\uc131\uacfc \ud6a8\uc728\uc131\uc744 \ub192\uc785\ub2c8\ub2e4. (\uc608: 0~1 \uc0ac\uc774 \uac12\uc73c\ub85c \ubcc0\ud658, \ud3c9\uade0 0, \ud45c\uc900\ud3b8\ucc28 1\ub85c \ubcc0\ud658)<\/p>\n<\/li>\n<li>\n<p><strong>\ud53c\ucc98 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1(Feature Engineering):<\/strong> \uae30\uc874 \ub370\uc774\ud130\ub97c \uc870\ud569\ud558\uac70\ub098 \uac00\uacf5\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ud2b9\uc9d5(feature)\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc774\ub294 \ubaa8\ub378\uc758 \uc608\uce21 \uc131\ub2a5\uc744 \ud06c\uac8c \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. (\uc608: \ub0a0\uc9dc \ub370\uc774\ud130\uc5d0\uc11c \uc694\uc77c, \uc6d4, \uc5f0\ub3c4 \uc815\ubcf4 \ucd94\ucd9c, \ub450 \ubcc0\uc218\uc758 \ube44\uc728 \uacc4\uc0b0)<\/p>\n<\/li>\n<li>\n<p><strong>\ubc94\uc8fc\ud615 \ub370\uc774\ud130 \uc778\ucf54\ub529(Categorical Data Encoding):<\/strong> &#8216;\ube68\uac15&#8217;, &#8216;\ud30c\ub791&#8217;, &#8216;\ucd08\ub85d&#8217;\uacfc \uac19\uc740 \ud14d\uc2a4\ud2b8 \ud615\ud0dc\uc758 \ubc94\uc8fc\ud615 \ub370\uc774\ud130\ub97c \uc22b\uc790 \ud615\ud0dc\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4. (\uc608: \uc6d0-\ud56b \uc778\ucf54\ub529, \ub808\uc774\ube14 \uc778\ucf54\ub529)<\/p>\n<\/li>\n<\/ul>\n<p><strong>\uc608\uc2dc:<\/strong> \uace0\uac1d\uc758 &#8216;\uad6c\ub9e4 \uae08\uc561&#8217;\uacfc &#8216;\uad6c\ub9e4 \ud69f\uc218&#8217;\ub77c\ub294 \ub450 \uac00\uc9c0 \ud53c\ucc98\uac00 \uc788\ub2e4\uba74, \uc774\ub97c \ud65c\uc6a9\ud558\uc5ec &#8216;\uace0\uac1d\ub2f9 \ud3c9\uade0 \uad6c\ub9e4 \uae08\uc561&#8217;\uc774\ub77c\ub294 \uc0c8\ub85c\uc6b4 \ud53c\ucc98\ub97c \ub9cc\ub4e4\uc5b4 \ubaa8\ub378\uc5d0 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>4. \ub370\uc774\ud130 \ub77c\ubca8\ub9c1 (Data Labeling)<\/h3>\n<p>\uc9c0\ub3c4 \ud559\uc2b5(Supervised Learning) AI \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130\uc5d0 &#8216;\uc815\ub2f5&#8217;\uc5d0 \ud574\ub2f9\ud558\ub294 \ub77c\ubca8(label)\uc744 \ubd99\uc5ec\uc57c \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 AI \ubaa8\ub378\uc758 \ud559\uc2b5 \ubc29\ud5a5\uc744 \uacb0\uc815\ud558\ub294 \ub9e4\uc6b0 \uc911\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uc815\uc758:<\/strong> \uc774\ubbf8\uc9c0 \ubd84\ub958 \ubaa8\ub378\uc744 \uc704\ud574 &#8220;\uc774\uac83\uc740 \uace0\uc591\uc774 \uc0ac\uc9c4\uc774\ub2e4&#8221;\ub77c\uace0 \ud45c\uc2dc\ud558\uac70\ub098, \uc2a4\ud338 \uba54\uc77c \ubd84\ub958 \ubaa8\ub378\uc744 \uc704\ud574 &#8220;\uc774 \uba54\uc77c\uc740 \uc2a4\ud338\uc774\ub2e4&#8221;\ub77c\uace0 \ud45c\uc2dc\ud558\ub294 \uc791\uc5c5\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ubc29\ubc95:<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>\ub0b4\ubd80 \ud300 \ud65c\uc6a9:<\/strong> \uc790\uccb4 \uc778\ub825\uc744 \ud22c\uc785\ud558\uc5ec \ub77c\ubca8\ub9c1\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc544\uc6c3\uc18c\uc2f1:<\/strong> \uc804\ubb38 \ub77c\ubca8\ub9c1 \uc11c\ube44\uc2a4 \uc5c5\uccb4\uc5d0 \uc704\ud0c1\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud06c\ub77c\uc6b0\ub4dc\uc18c\uc2f1:<\/strong> \ub2e4\uc218\uc758 \uc77c\ubc18\uc778\uc5d0\uac8c \uc791\uc5c5\uc744 \ubd84\ubc30\ud558\uc5ec \uc218\ud589\ud569\ub2c8\ub2e4. (\uc608: \uc544\ub9c8\uc874 Mechanical Turk)<\/p>\n<\/li>\n<li>\n<p><strong>\uc790\ub3d9 \ub77c\ubca8\ub9c1 \ub3c4\uad6c \ud65c\uc6a9:<\/strong> \ucd08\uae30 \ub77c\ubca8\ub9c1\uc744 \uc790\ub3d9\ud654\ud558\uace0, \uc0ac\ub78c\uc774 \uac80\uc218\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p><strong>\ub77c\ubca8\ub9c1\uc758 \uc911\uc694\uc131:<\/strong><\/p>\n<ul>\n<li>\n<p><strong>\uc815\ud655\uc131:<\/strong> \ub77c\ubca8\ub9c1\uc758 \uc815\ud655\ub3c4\uac00 AI \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc9c1\uc811\uc801\uc73c\ub85c \uc88c\uc6b0\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc77c\uad00\uc131:<\/strong> \uc5ec\ub7ec \uc0ac\ub78c\uc774 \ub77c\ubca8\ub9c1\ud560 \uacbd\uc6b0, \uc77c\uad00\ub41c \uae30\uc900\uc744 \uc801\uc6a9\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. (\uac00\uc774\ub4dc\ub77c\uc778 \uba85\ud655\ud654, \uac80\uc218 \ud504\ub85c\uc138\uc2a4 \uac15\ud654)<\/p>\n<\/li>\n<li>\n<p><strong>\uc804\ubb38\uc131:<\/strong> \ud2b9\uc815 \ubd84\uc57c\uc758 AI \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \ub54c\ub294 \ud574\ub2f9 \ubd84\uc57c\uc758 \uc804\ubb38\uac00\uac00 \ub77c\ubca8\ub9c1\uc5d0 \ucc38\uc5ec\ud558\ub294 \uac83\uc774 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p><strong>\uc608\uc2dc:<\/strong> \uc758\ub8cc \uc601\uc0c1 AI\ub97c \uac1c\ubc1c\ud560 \ub54c, \uc601\uc0c1\uc758\ud559\uacfc \uc804\ubb38\uc758\uac00 \ubcd1\ubcc0\uc758 \uc704\uce58\uc640 \uc885\ub958\ub97c \uc815\ud655\ud558\uac8c \ub77c\ubca8\ub9c1\ud574\uc57c AI\uac00 \uc815\ud655\ud55c \uc9c4\ub2e8\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>5. \ub370\uc774\ud130 \uac80\uc99d \ubc0f \ud3c9\uac00 (Data Validation &amp; Evaluation)<\/h3>\n<p>\uc900\ube44\ub41c \ub370\uc774\ud130\uac00 AI \ubaa8\ub378 \ud559\uc2b5\uc5d0 \uc801\ud569\ud55c\uc9c0, \uadf8\ub9ac\uace0 \ubaa9\ud45c \uc131\ub2a5\uc744 \ub2ec\uc131\ud560 \uc218 \uc788\ub294\uc9c0 \uac80\uc99d\ud558\ub294 \ub2e8\uacc4\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ub370\uc774\ud130 \ud488\uc9c8 \uac80\uc0ac:<\/strong> \uc815\uc81c \ubc0f \ubcc0\ud658 \uacfc\uc815\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc0c8\ub85c\uc6b4 \uc624\ub958\ub294 \uc5c6\ub294\uc9c0, \ub77c\ubca8\ub9c1\uc740 \uc815\ud655\ud55c\uc9c0 \ub4f1\uc744 \ub2e4\uc2dc \ud55c\ubc88 \ud655\uc778\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \ubd84\ud560:<\/strong> \ud559\uc2b5 \ub370\uc774\ud130(Training Data), \uac80\uc99d \ub370\uc774\ud130(Validation Data), \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130(Test Data)\ub85c \ub370\uc774\ud130\ub97c \ub098\ub215\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud559\uc2b5 \ub370\uc774\ud130:<\/strong> \ubaa8\ub378\uc774 \ud328\ud134\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. (\ubcf4\ud1b5 70-80%)<\/p>\n<\/li>\n<li>\n<p><strong>\uac80\uc99d \ub370\uc774\ud130:<\/strong> \ud559\uc2b5 \uc911\uac04\uc911\uac04 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. (\ubcf4\ud1b5 10-15%)<\/p>\n<\/li>\n<li>\n<p><strong>\ud14c\uc2a4\ud2b8 \ub370\uc774\ud130:<\/strong> \ucd5c\uc885 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \ub370 \uc0ac\uc6a9\ub418\uba70, \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c\ub294 \uc804\ud600 \uc0ac\uc6a9\ub418\uc9c0 \uc54a\uc740 \ub370\uc774\ud130\uc785\ub2c8\ub2e4. (\ubcf4\ud1b5 10-15%)<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \ubd84\ud3ec \ud655\uc778:<\/strong> \ud559\uc2b5, \uac80\uc99d, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b \uac04\uc758 \ub370\uc774\ud130 \ubd84\ud3ec\uac00 \uc720\uc0ac\ud55c\uc9c0 \ud655\uc778\ud558\uc5ec \ud3b8\ud5a5\uc744 \ubc29\uc9c0\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h3>6. \ub370\uc774\ud130 \uad00\ub9ac \ubc0f \uac70\ubc84\ub10c\uc2a4 (Data Management &amp; Governance)<\/h3>\n<p>AI \ud504\ub85c\uc81d\ud2b8\uac00 \uc9c4\ud589\ub428\uc5d0 \ub530\ub77c \ub370\uc774\ud130\ub294 \uc9c0\uc18d\uc801\uc73c\ub85c \uc0dd\uc131\ub418\uace0 \ubcc0\ud654\ud569\ub2c8\ub2e4. \uccb4\uacc4\uc801\uc778 \ub370\uc774\ud130 \uad00\ub9ac \ubc0f \uac70\ubc84\ub10c\uc2a4 \uad6c\ucd95\uc740 \uc7a5\uae30\uc801\uc778 AI \uc131\uacf5\uc744 \uc704\ud574 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ub370\uc774\ud130 \uc800\uc7a5\uc18c:<\/strong> \ub370\uc774\ud130\ub97c \uc548\uc804\ud558\uace0 \ud6a8\uc728\uc801\uc73c\ub85c \uc800\uc7a5\ud558\uace0 \uad00\ub9ac\ud560 \uc218 \uc788\ub294 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. (\ub370\uc774\ud130 \ub808\uc774\ud06c, \ub370\uc774\ud130 \uc6e8\uc5b4\ud558\uc6b0\uc2a4 \ub4f1)<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \uce74\ud0c8\ub85c\uadf8:<\/strong> \ub370\uc774\ud130\uc758 \ucd9c\ucc98, \ub0b4\uc6a9, \ud2b9\uc131, \uc0ac\uc6a9 \uc774\ub825 \ub4f1\uc744 \uae30\ub85d\ud558\uc5ec \ub370\uc774\ud130 \uac80\uc0c9 \ubc0f \uc774\ud574\ub97c \ub3d5\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \uc811\uadfc \uc81c\uc5b4:<\/strong> \ubbfc\uac10\ud55c \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc811\uadfc \uad8c\ud55c\uc744 \uad00\ub9ac\ud558\uc5ec \ubcf4\uc548\uc744 \uac15\ud654\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \ubc84\uc804 \uad00\ub9ac:<\/strong> \ub370\uc774\ud130\uc758 \ubcc0\uacbd \uc774\ub825\uc744 \ucd94\uc801\ud558\uace0 \uad00\ub9ac\ud558\uc5ec \uc7ac\ud604\uc131\uc744 \ud655\ubcf4\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uaddc\uc81c \uc900\uc218:<\/strong> \uac1c\uc778\uc815\ubcf4\ubcf4\ud638\ubc95 \ub4f1 \uad00\ub828 \ubc95\uaddc \ubc0f \uaddc\uc81c\ub97c \uc900\uc218\ud558\uba70 \ub370\uc774\ud130\ub97c \uad00\ub9ac\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p><strong>\ub370\uc774\ud130 \uac70\ubc84\ub10c\uc2a4\ub780?<\/strong> \ub370\uc774\ud130\uc758 \uac00\uc6a9\uc131, \uc0ac\uc6a9\uc131, \ubb34\uacb0\uc131, \ubcf4\uc548\uc744 \ubcf4\uc7a5\ud558\uae30 \uc704\ud55c \uc815\ucc45, \ud504\ub85c\uc138\uc2a4, \ud45c\uc900, \ucc45\uc784 \ub4f1\uc744 \uc815\uc758\ud558\uace0 \uad00\ub9ac\ud558\ub294 \uccb4\uacc4\uc785\ub2c8\ub2e4.<\/p>\n<h2>\ud754\ud55c \uc2e4\uc218\uc640 \uc8fc\uc758\uc0ac\ud56d<\/h2>\n<p>AI \uc900\ube44 \ub370\uc774\ud130\ub97c \uad6c\ucd95\ud558\ub294 \uacfc\uc815\uc5d0\uc11c \ub9ce\uc740 \uc0ac\ub78c\ub4e4\uc774 \ub2e4\uc74c\uacfc \uac19\uc740 \uc2e4\uc218\ub97c \uc800\uc9c0\ub974\uace4 \ud569\ub2c8\ub2e4.<\/p>\n<h3>1. &#8216;\ub370\uc774\ud130\ub294 \ub9ce\uc744\uc218\ub85d \uc88b\ub2e4&#8217;\ub294 \uc624\ud574<\/h3>\n<p>\ubb34\uc870\uac74 \ub9ce\uc740 \ub370\uc774\ud130\ub97c \ubaa8\uc73c\ub294 \uac83\ubcf4\ub2e4, <strong>\uc9c8 \uc88b\uc740 \ub370\uc774\ud130<\/strong>\ub97c \ud655\ubcf4\ud558\ub294 \uac83\uc774 \ud6e8\uc52c \uc911\uc694\ud569\ub2c8\ub2e4. \uc798\ubabb\ub41c \ub370\uc774\ud130\uac00 \ub9ce\uc73c\uba74 \uc624\ud788\ub824 \ubaa8\ub378 \uc131\ub2a5\uc744 \uc800\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2. \ucd08\uae30 \ub2e8\uacc4\uc5d0\uc11c\uc758 \ub370\uc774\ud130 \uc815\uc81c \uc18c\ud640<\/h3>\n<p>\ub370\uc774\ud130 \uc815\uc81c\ub294 \uc2dc\uac04\uacfc \ub178\ub825\uc774 \ub9ce\uc774 \ub4dc\ub294 \uc791\uc5c5\uc774\uc9c0\ub9cc, \uc774 \ub2e8\uacc4\ub97c \uc18c\ud640\ud788 \ud558\uba74 \uc774\ud6c4 \uacfc\uc815\uc5d0\uc11c \ud6e8\uc52c \ub354 \ud070 \ubb38\uc81c\uc5d0 \uc9c1\uba74\ud558\uac8c \ub429\ub2c8\ub2e4. &#8216;\ubbf8\ub9ac\ubbf8\ub9ac&#8217; \uc815\uc81c\ud558\ub294 \uc2b5\uad00\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n<h3>3. \ub77c\ubca8\ub9c1 \ud488\uc9c8 \uad00\ub9ac \ubd80\uc871<\/h3>\n<p>\ub77c\ubca8\ub9c1\uc740 AI \ubaa8\ub378\uc758 &#8216;\uc120\uc0dd\ub2d8&#8217;\uacfc \uac19\uc2b5\ub2c8\ub2e4. \uc120\uc0dd\ub2d8\uc758 \uac00\ub974\uce68\uc774 \uc798\ubabb\ub418\uba74 \ud559\uc0dd(AI \ubaa8\ub378)\uc740 \uc62c\ubc14\ub974\uac8c \ubc30\uc6b8 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4. \ub77c\ubca8\ub9c1 \uac00\uc774\ub4dc\ub77c\uc778\uc744 \uba85\ud655\ud788 \ud558\uace0, \uc9c0\uc18d\uc801\uc778 \uac80\uc218\uc640 \ud53c\ub4dc\ubc31\uc744 \ud1b5\ud574 \ud488\uc9c8\uc744 \uad00\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<h3>4. \ub370\uc774\ud130 \ud3b8\ud5a5\uc131 \uac04\uacfc<\/h3>\n<p>\uc790\uc2e0\ub3c4 \ubaa8\ub974\ub294 \uc0ac\uc774\uc5d0 \ub370\uc774\ud130\uc5d0 \ud3b8\ud5a5\uc774 \ud3ec\ud568\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uad00\uc810\uc5d0\uc11c \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0, \uc7a0\uc7ac\uc801\uc778 \ud3b8\ud5a5\uc131\uc744 \uc778\uc9c0\ud558\uba70 \uc774\ub97c \uc644\ud654\ud558\ub824\ub294 \ub178\ub825\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n<h3>5. \ub370\uc774\ud130 \uad00\ub9ac \uc2dc\uc2a4\ud15c \ubd80\uc7ac<\/h3>\n<p>AI \ud504\ub85c\uc81d\ud2b8\uac00 \ucee4\uc9c8\uc218\ub85d \ub370\uc774\ud130\ub294 \uae30\ud558\uae09\uc218\uc801\uc73c\ub85c \ub298\uc5b4\ub0a9\ub2c8\ub2e4. \uccb4\uacc4\uc801\uc778 \uad00\ub9ac \uc2dc\uc2a4\ud15c \uc5c6\uc774\ub294 \ub370\uc774\ud130\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \ud65c\uc6a9\ud558\uae30 \uc5b4\ub835\uace0, \ubcf4\uc548 \ubb38\uc81c\uae4c\uc9c0 \ubc1c\uc0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>AI \uc900\ube44 \ub370\uc774\ud130, \ub204\uac00 \uad6c\ucd95\ud574\uc57c \ud560\uae4c?<\/h2>\n<p>AI \uc900\ube44 \ub370\uc774\ud130 \uad6c\ucd95\uc740 \ub2e8\uc21c\ud788 \ub370\uc774\ud130 \uc5d4\uc9c0\ub2c8\uc5b4\ub9cc\uc758 \ubaab\uc774 \uc544\ub2d9\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc804\ubb38\uac00\ub4e4\uc758 \ud611\uc5c5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ub370\uc774\ud130 \uc5d4\uc9c0\ub2c8\uc5b4:<\/strong> \ub370\uc774\ud130 \ud30c\uc774\ud504\ub77c\uc778 \uad6c\ucd95, \uc800\uc7a5, \uad00\ub9ac \ub4f1 \uae30\uc220\uc801\uc778 \ubd80\ubd84\uc744 \ub2f4\ub2f9\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \uacfc\ud559\uc790\/ML \uc5d4\uc9c0\ub2c8\uc5b4:<\/strong> \ubaa8\ub378 \ud559\uc2b5\uc5d0 \ud544\uc694\ud55c \ub370\uc774\ud130 \uc694\uad6c\uc0ac\ud56d\uc744 \uc815\uc758\ud558\uace0, \ud53c\ucc98 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \ub4f1\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub3c4\uba54\uc778 \uc804\ubb38\uac00:<\/strong> \ub370\uc774\ud130\uc758 \uc758\ubbf8\ub97c \uc774\ud574\ud558\uace0, \ub77c\ubca8\ub9c1\uc758 \uc815\ud655\uc131\uc744 \ub192\uc774\uba70, \ud3b8\ud5a5\uc131\uc744 \ud310\ub2e8\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ube44\uc988\ub2c8\uc2a4 \ubd84\uc11d\uac00:<\/strong> AI \ubaa8\ub378\uc774 \ud574\uacb0\ud574\uc57c \ud560 \ube44\uc988\ub2c8\uc2a4 \ubb38\uc81c\ub97c \uc815\uc758\ud558\uace0, \ud544\uc694\ud55c \ub370\uc774\ud130\uc758 \uc6b0\uc120\uc21c\uc704\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\uc774 \ubaa8\ub4e0 \uc774\ud574\uad00\uacc4\uc790\ub4e4\uc774 \uae34\ubc00\ud558\uac8c \uc18c\ud1b5\ud558\uace0 \ud611\ub825\ud560 \ub54c, \ube44\ub85c\uc18c \uc81c\ub300\ub85c \uc900\ube44\ub41c AI \ub370\uc774\ud130\ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\uacb0\ub860: AI \uc131\uacf5\uc758 \uccab\uac78\uc74c, &#8216;\uc900\ube44\ub41c \ub370\uc774\ud130&#8217;\uc5d0 \ub2ec\ub824\uc788\ub2e4<\/h2>\n<p>AI \ubaa8\ub378\uc758 \ubc1c\uc804\uc740 \ub180\ub78d\uc9c0\ub9cc, \uadf8 \uadfc\uac04\uc5d0\ub294 &#8216;\uc798 \uc900\ube44\ub41c \ub370\uc774\ud130&#8217;\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ubb34\ub9ac \ub6f0\uc5b4\ub09c AI \ubaa8\ub378\ub3c4 \ubd80\uc815\ud655\ud558\uace0 \ud3b8\ud5a5\ub41c \ub370\uc774\ud130\ub85c\ub294 \uc81c \uc5ed\ud560\uc744 \ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4. AI \uc900\ube44 \ub370\uc774\ud130\ub294 \ub2e8\uc21c\ud788 \ub370\uc774\ud130\ub97c \ubaa8\uc73c\ub294 \uac83\uc744 \ub118\uc5b4, <strong>\uc218\uc9d1, \uc815\uc81c, \ubcc0\ud658, \ub77c\ubca8\ub9c1, \uac80\uc99d, \uad00\ub9ac<\/strong>\uc5d0 \uc774\ub974\ub294 \uccb4\uacc4\uc801\uc778 \uacfc\uc815\uc744 \ud1b5\ud574 \ub9cc\ub4e4\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n<p>\uc131\uacf5\uc801\uc778 AI \uad6c\ucd95\uc744 \uc6d0\ud55c\ub2e4\uba74, \ub2e4\uc74c \ub2e8\uacc4\ub97c \uae30\uc5b5\ud558\uc138\uc694.<\/p>\n<ol>\n<li>\n<p><strong>\ub370\uc774\ud130\uc758 \uc911\uc694\uc131\uc744 \uc778\uc2dd\ud558\uace0, &#8216;\uc9c8 \uc88b\uc740 \ub370\uc774\ud130&#8217; \ud655\ubcf4\uc5d0 \uc9d1\uc911\ud558\uc2ed\uc2dc\uc624.<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \uc815\uc81c \ubc0f \ub77c\ubca8\ub9c1 \uacfc\uc815\uc744 \uc18c\ud640\ud788 \ud558\uc9c0 \ub9c8\uc2ed\uc2dc\uc624.<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130\uc758 \ud3b8\ud5a5\uc131\uc744 \uc778\uc9c0\ud558\uace0, \uc774\ub97c \uc644\ud654\ud558\ub824\ub294 \ub178\ub825\uc744 \uae30\uc6b8\uc774\uc2ed\uc2dc\uc624.<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>\uccb4\uacc4\uc801\uc778 \ub370\uc774\ud130 \uad00\ub9ac \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uc5ec \uc7a5\uae30\uc801\uc778 \ud65c\uc6a9 \uae30\ubc18\uc744 \ub9c8\ub828\ud558\uc2ed\uc2dc\uc624.<\/strong><\/p>\n<\/li>\n<\/ol>\n<p>AI \uc2dc\ub300\ub97c \uc120\ub3c4\ud558\ub294 \uae30\uc5c5\ub4e4\uc740 \uc774\ubbf8 &#8216;\ub370\uc774\ud130&#8217;\ub97c \uac00\uc7a5 \uc911\uc694\ud55c \uc790\uc0b0\uc73c\ub85c \uc5ec\uae30\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 AI \uc5ec\uc815\uc5d0\uc11c\ub3c4 &#8216;AI \uc900\ube44 \ub370\uc774\ud130&#8217; \uad6c\ucd95\uc744 \ucd5c\uc6b0\uc120 \uacfc\uc81c\ub85c \uc0bc\uc73c\uc2dc\uae38 \ubc14\ub78d\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:\/\/www.datanami.com\/2023\/01\/17\/the-future-of-ai-and-data-science\/\" target=\"_blank\" rel=\"noopener noreferrer\">AI\uc640 \ub370\uc774\ud130 \uacfc\ud559\uc758 \ubbf8\ub798<\/a>, <a href=\"https:\/\/towardsdatascience.com\/data-cleaning-techniques-for-data-science-a8865721b60\" target=\"_blank\" rel=\"noopener noreferrer\">\ub370\uc774\ud130 \uacfc\ud559\uc790\ub97c \uc704\ud55c \ub370\uc774\ud130 \uc815\uc81c \uac00\uc774\ub4dc<\/a>, <a href=\"https:\/\/www.ibm.com\/topics\/supervised-learning\" target=\"_blank\" rel=\"noopener noreferrer\">\uc9c0\ub3c4 \ud559\uc2b5\uc758 \uc6d0\ub9ac<\/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\">AI Model Performance Starts With \u201cGood Data\u201d<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Artificial intelligence (AI) is becoming deeply integrated into many areas of our lives. From autonomous vehicles to personalized recommendation services, AI is advancing at an astonishing pace. Many people assume that the heart of AI is a \u201csmart algorithm\u201d or a \u201cstate-of-the-art model.\u201d But AI experts consistently emphasize one point: <strong>no matter how good the model is, it will not work properly if the data is a mess.<\/strong> In other words, the success of an AI model depends not only on having a good model, but first on having <strong>well-prepared data<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What Is AI-Ready Data?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>AI-ready data<\/strong> refers to data that has been processed and refined so it can be used directly for training AI models. It goes beyond simply collecting large amounts of data. It includes the entire process of preparing data in an accurate and relevant form so that an AI model can perform a specific task effectively.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why Is AI-Ready Data More Important Than the Model?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">AI models learn from data and identify patterns within it. If the training data contains many errors, is biased, or includes irrelevant information, the model will learn the wrong patterns. This can lead to poor AI service performance, faulty predictions, or even discriminatory outcomes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. Data Determines the Quality of Learning<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI models follow the principle of <strong>\u201cGarbage In, Garbage Out.\u201d<\/strong> No matter how sophisticated the algorithm is, it cannot learn correctly from inaccurate or incomplete data. It is like trying to make a great meal with the wrong ingredients, even if the recipe is excellent.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Solving the Bias Problem<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If the data contains bias against a particular group, the AI model will also learn that bias and may produce discriminatory results. For example, if a hiring AI is trained on data that lacks or misrepresents certain racial or gender groups, it may produce unfair outcomes for those groups. Preparing AI-ready data requires recognizing and correcting such biases.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Reducing Development Time and Cost<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If data is properly prepared from the beginning, the time and cost spent fixing data-related problems during model development can be greatly reduced. Correcting data later is much more difficult than preparing it properly from the start.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Improving Reliability and Generalization<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI models trained on clean and well-refined data achieve higher accuracy and reliability. They are also better able to adapt to different situations and new data, which makes them more useful in real-world services.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Core Steps for Building AI-Ready Data<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Creating AI-ready data involves much more than simply collecting information. It requires a systematic process to maximize model performance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">1. Data Collection<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The first step is to gather data related to the problem the AI model is intended to solve. Data sources may vary.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Internal data:<\/strong> customer information, sales records, log data, and other data held by an organization<\/li>\n\n\n\n<li><strong>External data:<\/strong> public datasets, web scraping, sensor data, social media data, and more<\/li>\n\n\n\n<li><strong>Synthetic data:<\/strong> artificially generated data created through simulation when real data is scarce or sensitive<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Key points to watch<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Relevance of the data:<\/strong><br>Make sure the collected data is directly related to the AI model\u2019s objective.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Diversity of the data:<\/strong><br>It is important to collect data that covers a broad range of cases so the model does not become skewed toward only certain situations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Legality and ethics of the data:<\/strong><br>Data collection must comply with privacy regulations such as GDPR and CCPA, and data that could raise ethical concerns should not be collected carelessly.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">2. Data Cleaning<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Collected data often contains errors, missing values, duplicates, and outliers. Data cleaning removes these impurities and improves consistency and accuracy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Handling missing values:<\/strong><br>Missing values may be filled in or the affected records may be removed. Common methods include replacing them with the mean or median, interpolating using previous or next values, or deleting rows or columns.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detecting and handling outliers:<\/strong><br>Values outside the normal range are identified and either removed or adjusted using statistical techniques or visualization.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Removing duplicate data:<\/strong><br>When the same data is recorded multiple times, duplicates should be removed to improve accuracy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Standardizing data formats:<\/strong><br>Data formats such as dates, times, and units should be made consistent. For example, \u201c2023-10-27,\u201d \u201c27\/10\/2023,\u201d and \u201cOct 27, 2023\u201d should all be converted into a standard format like <code>YYYY-MM-DD<\/code>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Correcting typos and errors:<\/strong><br>Typographical mistakes and incorrect symbols in text data should be fixed.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example:<\/strong><br>If customer phone numbers appear in both <code>010-1234-5678<\/code> and <code>01012345678<\/code> formats, they should be standardized into a single consistent format such as <code>010-1234-5678<\/code>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">3. Data Transformation<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This is the process of converting cleaned data into a form that AI models can understand and learn from more effectively.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Normalization and standardization:<\/strong><br>Adjusting the range of data values improves stability and efficiency in model training. Examples include converting values into a 0\u20131 range or transforming them to have a mean of 0 and standard deviation of 1.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Feature engineering:<\/strong><br>New features are created by combining or transforming existing data. This can significantly improve prediction performance. Examples include extracting the day, month, and year from date data, or calculating the ratio between two variables.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Categorical data encoding:<\/strong><br>Text-based categories such as \u201cred,\u201d \u201cblue,\u201d and \u201cgreen\u201d are converted into numerical form using methods such as one-hot encoding or label encoding.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example:<\/strong><br>If a customer dataset contains the features \u201cpurchase amount\u201d and \u201cpurchase frequency,\u201d a new feature such as \u201caverage purchase amount per customer\u201d can be created and added to the model.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">4. Data Labeling<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To train a <strong>supervised learning<\/strong> AI model, data must be given labels that represent the correct answer. This is one of the most important steps because it determines the direction of model learning.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Definition:<\/strong><br>For an image classification model, this means marking an image as \u201cthis is a cat.\u201d For a spam classification model, it means labeling an email as \u201cthis email is spam.\u201d<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Common methods<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Internal teams:<\/strong> using in-house staff for labeling<\/li>\n\n\n\n<li><strong>Outsourcing:<\/strong> using specialized labeling service providers<\/li>\n\n\n\n<li><strong>Crowdsourcing:<\/strong> distributing tasks to many individuals, such as through Amazon Mechanical Turk<\/li>\n\n\n\n<li><strong>Automated labeling tools:<\/strong> using automation for initial labeling and then having humans review the results<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Why labeling quality matters<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Accuracy:<\/strong><br>The accuracy of labeling directly affects model performance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Consistency:<\/strong><br>If multiple people are labeling data, consistent standards are essential. Clear guidelines and stronger review processes help maintain consistency.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Expertise:<\/strong><br>When building AI for specialized fields, it is often important for domain experts to participate in labeling.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example:<\/strong><br>When developing medical imaging AI, a radiologist must accurately label the location and type of lesions so the model can learn to diagnose correctly.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">5. Data Validation and Evaluation<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This step verifies whether the prepared data is suitable for model training and whether it can support the desired performance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data quality checks:<\/strong><br>Make sure no new errors were introduced during cleaning or transformation, and confirm that labeling is accurate.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Splitting the dataset:<\/strong><br>The data is divided into three parts:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Training data:<\/strong> used for learning patterns, usually 70\u201380%<\/li>\n\n\n\n<li><strong>Validation data:<\/strong> used during training to evaluate performance and tune hyperparameters, usually 10\u201315%<\/li>\n\n\n\n<li><strong>Test data:<\/strong> used only for final performance evaluation, usually 10\u201315%<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Checking data distribution:<\/strong><br>The distributions of the training, validation, and test datasets should be similar to avoid bias.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">6. Data Management and Governance<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">As an AI project progresses, data continues to be generated and changed. Systematic data management and governance are essential for long-term AI success.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data repository:<\/strong><br>A system such as a data lake or data warehouse should be built to store and manage data safely and efficiently.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data catalog:<\/strong><br>Information about data origin, contents, characteristics, and usage history should be recorded to help users find and understand datasets.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data access control:<\/strong><br>Permissions for accessing sensitive data should be managed to strengthen security.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data version control:<\/strong><br>Changes to the data should be tracked and managed to ensure reproducibility.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Regulatory compliance:<\/strong><br>Data management must comply with relevant privacy laws and regulations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What is data governance?<\/strong><br>It is the framework of policies, processes, standards, and responsibilities that ensures data availability, usability, integrity, and security.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Common Mistakes and Cautions<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">People often make the following mistakes when building AI-ready data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. Believing \u201cMore Data Is Always Better\u201d<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">It is far more important to secure <strong>high-quality data<\/strong> than simply to collect a massive amount of it. A large amount of bad data can actually hurt model performance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Neglecting Data Cleaning Early On<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Cleaning data takes time and effort, but skipping it creates much bigger problems later. It is important to develop the habit of cleaning data early and properly.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Poor Quality Control in Labeling<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Labeling is like teaching an AI model. If the teacher is wrong, the student will learn incorrectly. Labeling guidelines must be clear, and quality should be maintained through continuous review and feedback.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Overlooking Data Bias<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Bias can be hidden in data without anyone realizing it. It is necessary to analyze data from multiple perspectives, identify possible biases, and actively work to reduce them.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">5. Lack of a Data Management System<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As AI projects grow, data expands exponentially. Without a systematic management approach, data becomes hard to use effectively and security issues may arise.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Who Should Build AI-Ready Data?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Building AI-ready data is not just the job of data engineers. It requires collaboration among multiple experts.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data engineers:<\/strong><br>Responsible for technical tasks such as building data pipelines, storage, and management.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data scientists \/ ML engineers:<\/strong><br>Define the data requirements for model training and perform tasks such as feature engineering.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Domain experts:<\/strong><br>Play a key role in understanding data meaning, improving labeling accuracy, and identifying bias.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Business analysts:<\/strong><br>Define the business problems the AI model should solve and determine the priority of required data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Only when all these stakeholders communicate and collaborate closely can truly well-prepared AI data be built.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion: The First Step Toward AI Success Depends on Prepared Data<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The development of AI models is impressive, but at the foundation lies <strong>well-prepared data<\/strong>. No matter how powerful a model is, it cannot perform properly if it is trained on inaccurate or biased data. AI-ready data is created not merely by collecting data, but through a systematic process of collection, cleaning, transformation, labeling, validation, and management.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If you want to build successful AI, remember the following:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Recognize the importance of data and focus on securing <strong>high-quality data<\/strong><\/li>\n\n\n\n<li>Do not neglect data cleaning and labeling<\/li>\n\n\n\n<li>Be aware of bias in data and actively try to reduce it<\/li>\n\n\n\n<li>Build a systematic data management system to support long-term use<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The companies leading the AI era already treat <strong>data<\/strong> as one of their most important assets. In any AI journey, building <strong>AI-ready data<\/strong> should be a top priority.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI \ubaa8\ub378 \uc131\ub2a5\uc744 \uc88c\uc6b0\ud558\ub294 \uac83\uc740 \uacb0\uad6d \ub370\uc774\ud130\uc785\ub2c8\ub2e4. \uc544\ubb34\ub9ac \ud6cc\ub96d\ud55c AI \ubaa8\ub378\uc774\ub77c\ub3c4 \uc81c\ub300\ub85c \uc900\ube44\ub418\uc9c0 \uc54a\uc740 \ub370\uc774\ud130 \uc55e\uc5d0\uc11c\ub294 \ubb34\uc6a9\uc9c0\ubb3c\uc774\uc8e0. \uc774 \uae00\uc5d0\uc11c\ub294 AI \uc900\ube44 \ub370\uc774\ud130\uc758 \uc911\uc694\uc131\uc744 \uac15\uc870\ud558\uace0, \ub370\uc774\ud130 \uc815\uc81c, \ub77c\ubca8\ub9c1, \uad00\ub9ac \ub4f1 \uc131\uacf5\uc801\uc778 AI \uad6c\ucd95\uc744 \uc704\ud55c \ud544\uc218 \uc694\uc18c\ub4e4\uc744 \uc0c1\uc138\ud788 \uc548\ub0b4\ud569\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":[507,500,384,390,385,503,502,501,504,506,505,90,389,388,387,386,391,55,35],"class_list":["post-118","post","type-post","status-publish","format-standard","hentry","category-ai","tag-ai-data","tag-ai-models","tag-ai-","tag-ai--","tag-ai-ready-data","tag-data-cleaning","tag-data-governance","tag-data-labeling","tag-data-management","tag-data-quality","tag-deep-learning","tag-389","tag-388","tag-387","tag-386","tag-391","tag-55","tag-35"],"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\/118","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=118"}],"version-history":[{"count":2,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/118\/revisions"}],"predecessor-version":[{"id":147,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/118\/revisions\/147"}],"wp:attachment":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=118"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=118"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=118"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}