{"id":105,"date":"2026-04-22T04:27:25","date_gmt":"2026-04-21T19:27:25","guid":{"rendered":"https:\/\/ai-cloud.kr\/?p=105"},"modified":"2026-04-22T04:27:26","modified_gmt":"2026-04-21T19:27:26","slug":"%ed%8f%89%ea%b0%80-%ec%a4%91%ec%8b%ac-ai-%ea%b0%9c%eb%b0%9c-%ec%a0%9c%eb%8c%80%eb%a1%9c-%ec%b8%a1%ec%a0%95%ed%95%b4%ec%95%bc-%ec%a7%84%ec%a7%9c-%ec%84%b1%ea%b3%b5%ed%95%9c%eb%8b%a4evaluation-driven","status":"publish","type":"post","link":"https:\/\/ai-cloud.kr\/?p=105","title":{"rendered":"\ud3c9\uac00 \uc911\uc2ec AI \uac1c\ubc1c: \uc81c\ub300\ub85c \uce21\uc815\ud574\uc57c \uc9c4\uc9dc \uc131\uacf5\ud55c\ub2e4(Evaluation-Driven AI Development: You Need to Measure Properly to Achieve Real Success)"},"content":{"rendered":"<h2>AI \uac1c\ubc1c, \uc18d\ub3c4\ubcf4\ub2e4 \uc911\uc694\ud55c &#8216;\ud3c9\uac00&#8217;\uc758 \uc7ac\ubc1c\uacac<\/h2>\n<p>\uc778\uacf5\uc9c0\ub2a5(AI) \uac1c\ubc1c \uacbd\uc7c1\uc774 \uce58\uc5f4\ud574\uc9c0\uba74\uc11c &#8216;\uc5bc\ub9c8\ub098 \ube68\ub9ac \ub9cc\ub4e4 \uc218 \uc788\ub294\uac00&#8217;\uc5d0 \ub300\ud55c \uad00\uc2ec\uc774 \ub192\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ub9ce\uc740 \uc804\ubb38\uac00\ub4e4\uc740 \uc774\uc81c \uc18d\ub3c4 \uacbd\uc7c1\ubcf4\ub2e4\ub294 &#8216;\uc81c\ub300\ub85c \ub9cc\ub4dc\ub294 \uac83&#8217;, \uc989 AI\uc758 \uc131\ub2a5\uacfc \uac00\uce58\ub97c \uc815\ud655\ud558\uac8c \uce21\uc815\ud558\ub294 \uac83\uc774 \ud6e8\uc52c \uc911\uc694\ud558\ub2e4\uace0 \uac15\uc870\ud569\ub2c8\ub2e4. \ubc14\ub85c &#8216;\ud3c9\uac00 \uc911\uc2ec AI \uac1c\ubc1c(Evaluation-Driven AI Development)&#8217;\uc774\ub77c\ub294 \uac1c\ub150\uc774 \uc8fc\ubaa9\ubc1b\ub294 \uc774\uc720\uc785\ub2c8\ub2e4.<\/p>\n<h3>\uc65c &#8216;\uc798 \ub9cc\ub4dc\ub294 \uac83&#8217;\ubcf4\ub2e4 &#8216;\uc81c\ub300\ub85c \uce21\uc815\ud558\ub294 \uac83&#8217;\uc774 \uc911\uc694\ud560\uae4c\uc694?<\/h3>\n<p>AI \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\ub294 \uacfc\uc815\uc740 \ub2e8\uc21c\ud788 \ucf54\ub4dc\ub97c \uc791\uc131\ud558\uace0 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\ub294 \uac83 \uc774\uc0c1\uc785\ub2c8\ub2e4. AI\ub294 \ud604\uc2e4 \uc138\uacc4\uc758 \ubcf5\uc7a1\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uace0 \uac00\uce58\ub97c \ucc3d\ucd9c\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574\uc11c\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \uc2e4\uc81c \ube44\uc988\ub2c8\uc2a4 \ubaa9\ud45c\uc640 \uc5bc\ub9c8\ub098 \ubd80\ud569\ud558\ub294\uc9c0, \uc608\uc0c1\uce58 \ubabb\ud55c \ubd80\uc791\uc6a9\uc740 \uc5c6\ub294\uc9c0 \ub4f1\uc744 \uac1d\uad00\uc801\uc73c\ub85c \ud3c9\uac00\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ubaa9\ud45c \ub2ec\uc131 \uc5ec\ubd80 \ud655\uc778:<\/strong> AI \ubaa8\ub378\uc774 \ud2b9\uc815 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \uac1c\ubc1c\ub418\uc5c8\ub2e4\uba74, \uadf8 \ubb38\uc81c\ub97c \uc5bc\ub9c8\ub098 \ud6a8\uacfc\uc801\uc73c\ub85c \ud574\uacb0\ud558\ub294\uc9c0 \uce21\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc790\uc728 \uc8fc\ud589 \uc790\ub3d9\ucc28\uc758 AI\ub77c\uba74 \uc5bc\ub9c8\ub098 \uc548\uc804\ud558\uac8c \uc6b4\uc804\ud558\ub294\uc9c0, \uc5bc\ub9c8\ub098 \ud6a8\uc728\uc801\uc73c\ub85c \uacbd\ub85c\ub97c \ud0d0\uc0c9\ud558\ub294\uc9c0 \ub4f1\uc744 \uce21\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc790\uc6d0 \ub0ad\ube44 \ubc29\uc9c0:<\/strong> \uc131\ub2a5\uc774 \uac80\uc99d\ub418\uc9c0 \uc54a\uc740 AI \ubaa8\ub378\uc5d0 \ub9c9\ub300\ud55c \uc2dc\uac04\uacfc \ube44\uc6a9\uc744 \ud22c\uc790\ud558\ub294 \uac83\uc740 \ub0ad\ube44\uc785\ub2c8\ub2e4. \uccb4\uacc4\uc801\uc778 \ud3c9\uac00\ub294 \ucd08\uae30 \ub2e8\uacc4\uc5d0\uc11c \ubb38\uc81c\uc810\uc744 \ud30c\uc545\ud558\uace0 \uac1c\uc120\ud558\uc5ec \ubd88\ud544\uc694\ud55c \uc790\uc6d0 \ud22c\uc785\uc744 \ub9c9\uc544\uc90d\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc2e0\ub8b0\uc131 \ubc0f \uc548\uc804\uc131 \ud655\ubcf4:<\/strong> AI \ubaa8\ub378\uc740 \uc6b0\ub9ac \uc0b6\uc758 \ub2e4\uc591\ud55c \uc601\uc5ed\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \ud3b8\ud5a5\ub418\uac70\ub098 \uc798\ubabb\ub41c \ud310\ub2e8\uc744 \ub0b4\ub9ac\ub294 AI\ub294 \uc2ec\uac01\ud55c \ubb38\uc81c\ub97c \uc57c\uae30\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c AI\uc758 \uc2e0\ub8b0\uc131\uacfc \uc548\uc804\uc131\uc744 \ucca0\uc800\ud788 \uac80\uc99d\ud558\ub294 \ud3c9\uac00\ub294 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc9c0\uc18d\uc801\uc778 \uac1c\uc120:<\/strong> AI \ubaa8\ub378\uc740 \ud55c \ubc88 \uac1c\ubc1c\ud558\uace0 \ub05d\ub098\ub294 \uac83\uc774 \uc544\ub2d9\ub2c8\ub2e4. \uc2e4\uc81c \ud658\uacbd\uc5d0\uc11c \uc9c0\uc18d\uc801\uc73c\ub85c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uc131\ub2a5\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud558\uba70 \uac1c\uc120\ud574\uc57c \ud569\ub2c8\ub2e4. \ud6a8\uacfc\uc801\uc778 \ud3c9\uac00 \uccb4\uacc4\ub294 \uc774\ub7ec\ud55c \uc9c0\uc18d\uc801\uc778 \uac1c\uc120\uc744 \uc704\ud55c \uae30\ubc18\uc774 \ub429\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h3>\ud3c9\uac00 \uc911\uc2ec AI \uac1c\ubc1c, \uc5b4\ub5bb\uac8c \uc2dc\uc791\ud574\uc57c \ud560\uae4c\uc694?<\/h3>\n<p>\ud3c9\uac00 \uc911\uc2ec AI \uac1c\ubc1c\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub97c \ud1b5\ud574 \uccb4\uacc4\uc801\uc73c\ub85c \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h4>1. \uba85\ud655\ud55c \ubaa9\ud45c \uc124\uc815 \ubc0f \ud575\uc2ec \uc131\uacfc \uc9c0\ud45c(KPI) \uc815\uc758<\/h4>\n<p>\uac00\uc7a5 \uba3c\uc800 AI \ubaa8\ub378\uc774 \ub2ec\uc131\ud574\uc57c \ud560 \uad6c\uccb4\uc801\uc778 \ubaa9\ud45c\ub97c \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \ubaa9\ud45c\ub294 \uce21\uc815 \uac00\ub2a5\ud574\uc57c \ud558\uba70, \ube44\uc988\ub2c8\uc2a4 \ubaa9\ud45c\uc640 \uc9c1\uc811\uc801\uc73c\ub85c \uc5f0\uacb0\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uc608\uc2dc:<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>\ubaa9\ud45c:<\/strong> \uace0\uac1d \ubb38\uc758 \uc751\ub300 \ucc57\ubd07\uc758 \ub9cc\uc871\ub3c4 20% \ud5a5\uc0c1<\/p>\n<\/li>\n<li>\n<p><strong>KPI:<\/strong> \uace0\uac1d \ub9cc\uc871\ub3c4 \uc810\uc218, \ubb38\uc758 \ud574\uacb0 \uc2dc\uac04, \uc7ac\ubb38\uc758\uc728<\/p>\n<\/li>\n<li>\n<p><strong>\ubaa9\ud45c:<\/strong> \uc81c\uc870 \uacf5\uc815 \ubd88\ub7c9\ub960 15% \uac10\uc18c<\/p>\n<\/li>\n<li>\n<p><strong>KPI:<\/strong> \ubd88\ub7c9\ud488 \uac80\ucd9c \uc815\ud655\ub3c4, \uc624\uac80\ucd9c\ub960, \uac80\uc0ac \uc2dc\uac04<\/p>\n<\/li>\n<\/ul>\n<h4>2. \uc801\uc808\ud55c \ud3c9\uac00 \uc9c0\ud45c \ubc0f \ubc29\ubc95\ub860 \uc120\ud0dd<\/h4>\n<p>\ubaa9\ud45c\uc640 KPI\uc5d0 \ub9de\ucdb0 \uc5b4\ub5a4 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud558\uc5ec AI \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uce21\uc815\ud560\uc9c0 \uacb0\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e8\uc21c\ud788 \uc815\ud655\ub3c4(Accuracy)\ub9cc \ubcf4\ub294 \uac83\uc774 \uc544\ub2c8\ub77c, \ubb38\uc81c\uc758 \ud2b9\uc131\uc5d0 \ub9de\ub294 \ub2e4\uc591\ud55c \uc9c0\ud45c\ub97c \uace0\ub824\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uc8fc\uc694 \ud3c9\uac00 \uc9c0\ud45c:<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>\uc815\ud655\ub3c4 (Accuracy):<\/strong> \uc804\uccb4 \uc608\uce21 \uc911 \uc62c\ubc14\ub974\uac8c \uc608\uce21\ud55c \ube44\uc728 (\ubd84\ub958 \ubb38\uc81c\uc5d0\uc11c \uae30\ubcf8\uc801\uc73c\ub85c \uc0ac\uc6a9)<\/p>\n<\/li>\n<li>\n<p><strong>\uc815\ubc00\ub3c4 (Precision):<\/strong> \ubaa8\ub378\uc774 &#8216;\uae0d\uc815&#8217;\uc73c\ub85c \uc608\uce21\ud55c \uac83 \uc911 \uc2e4\uc81c &#8216;\uae0d\uc815&#8217;\uc778 \ube44\uc728 (\uc624\ud0d0\uc744 \uc904\uc774\ub294 \uac83\uc774 \uc911\uc694\ud560 \ub54c)<\/p>\n<\/li>\n<li>\n<p><strong>\uc7ac\ud604\uc728 (Recall):<\/strong> \uc2e4\uc81c &#8216;\uae0d\uc815&#8217;\uc778 \uac83 \uc911 \ubaa8\ub378\uc774 &#8216;\uae0d\uc815&#8217;\uc73c\ub85c \uc608\uce21\ud55c \ube44\uc728 (\ubbf8\ud0d0\uc744 \uc904\uc774\ub294 \uac83\uc774 \uc911\uc694\ud560 \ub54c)<\/p>\n<\/li>\n<li>\n<p><strong>F1-Score:<\/strong> \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\uc728\uc758 \uc870\ud654 \ud3c9\uade0 (\ub450 \uc9c0\ud45c\uac00 \ubaa8\ub450 \uc911\uc694\ud560 \ub54c)<\/p>\n<\/li>\n<li>\n<p><strong>ROC \uace1\uc120 \ubc0f AUC:<\/strong> \uc774\uc9c4 \ubd84\ub958 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc804\ubc18\uc801\uc73c\ub85c \ud3c9\uac00 (\ub2e4\uc591\ud55c \uc784\uacc4\uac12\uc5d0\uc11c\uc758 \uc131\ub2a5\uc744 \ube44\uad50)<\/p>\n<\/li>\n<li>\n<p><strong>MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error):<\/strong> \ud68c\uadc0 \ubb38\uc81c\uc5d0\uc11c \uc608\uce21\uac12\uacfc \uc2e4\uc81c\uac12\uc758 \ucc28\uc774\ub97c \uce21\uc815<\/p>\n<\/li>\n<li>\n<p><strong>\ud3c9\uac00 \ubc29\ubc95\ub860:<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>\uad50\ucc28 \uac80\uc99d (Cross-Validation):<\/strong> \ub370\uc774\ud130\ub97c \uc5ec\ub7ec \uac1c\uc758 \ud3f4\ub4dc(fold)\ub85c \ub098\ub204\uc5b4 \ud559\uc2b5\uacfc \ud3c9\uac00\ub97c \ubc18\ubcf5\ud568\uc73c\ub85c\uc368 \ubaa8\ub378\uc758 \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ub192\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>A\/B \ud14c\uc2a4\ud2b8:<\/strong> \ub450 \uac00\uc9c0 \uc774\uc0c1\uc758 \ubaa8\ub378 \ub610\ub294 \ubc84\uc804\uc744 \uc2e4\uc81c \uc0ac\uc6a9\uc790 \ud658\uacbd\uc5d0\uc11c \ube44\uad50\ud558\uc5ec \uc5b4\ub5a4 \uac83\uc774 \ub354 \ub098\uc740 \uc131\ub2a5\uc744 \ubcf4\uc774\ub294\uc9c0 \uce21\uc815\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc2dc\ubbac\ub808\uc774\uc158:<\/strong> \uc2e4\uc81c \ud658\uacbd\uacfc \uc720\uc0ac\ud55c \uc870\uac74\uc5d0\uc11c \ubaa8\ub378\uc744 \ud14c\uc2a4\ud2b8\ud558\uc5ec \uc131\ub2a5\uc744 \uc608\uce21\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h4>3. \ub370\uc774\ud130\uc14b \uc900\ube44 \ubc0f \uad00\ub9ac<\/h4>\n<p>\ud3c9\uac00\uc758 \uc2e0\ub8b0\uc131\uc740 \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc5d0 \ud06c\uac8c \uc88c\uc6b0\ub429\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ud559\uc2b5 \ub370\uc774\ud130 (Training Data):<\/strong> \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ub370 \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uac80\uc99d \ub370\uc774\ud130 (Validation Data):<\/strong> \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc911\uac04 \uc810\uac80\ud558\uace0 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \ud29c\ub2dd\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 (Test Data):<\/strong> \ucd5c\uc885 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac1d\uad00\uc801\uc73c\ub85c \ud3c9\uac00\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub294 \ud559\uc2b5 \ubc0f \uac80\uc99d \uacfc\uc815\uc5d0\uc11c \uc808\ub300 \uc0ac\uc6a9\ub418\uc9c0 \uc54a\uc544\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \ud488\uc9c8 \uad00\ub9ac:<\/strong> \ub370\uc774\ud130\uc758 \ud3b8\ud5a5\uc131, \ub178\uc774\uc988, \ub204\ub77d\uac12 \ub4f1\uc744 \ucca0\uc800\ud788 \uad00\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h4>4. \uc9c0\uc18d\uc801\uc778 \ubaa8\ub2c8\ud130\ub9c1 \ubc0f \uc7ac\ud3c9\uac00<\/h4>\n<p>AI \ubaa8\ub378\uc740 \ubc30\ud3ec \ud6c4\uc5d0\ub3c4 \uc131\ub2a5\uc774 \uc800\ud558\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \ubd84\ud3ec\uc758 \ubcc0\ud654(Data Drift)\ub098 \uac1c\ub150\uc758 \ubcc0\ud654(Concept Drift) \ub4f1\uc73c\ub85c \uc778\ud574 \ubaa8\ub378\uc758 \uc608\uce21\uc774 \uc2e4\uc81c \ud658\uacbd\uacfc \ub9de\uc9c0 \uc54a\uac8c \ub420 \uc218 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uc2e4\uc2dc\uac04 \ubaa8\ub2c8\ud130\ub9c1:<\/strong> \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc, \uc785\ub825 \ub370\uc774\ud130\uc758 \ud2b9\uc131 \ubcc0\ud654 \ub4f1\uc744 \uc2e4\uc2dc\uac04\uc73c\ub85c \ucd94\uc801\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc815\uae30\uc801\uc778 \uc7ac\ud3c9\uac00:<\/strong> \uc8fc\uae30\uc801\uc73c\ub85c \ucd5c\uc2e0 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc7ac\ud3c9\uac00\ud558\uace0, \ud544\uc694\ud558\ub2e4\uba74 \ubaa8\ub378\uc744 \uc7ac\ud559\uc2b5\ud558\uac70\ub098 \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h3>\uc2e4\uc81c \uc131\uacf5 \uc0ac\ub840: \ud3c9\uac00 \uc911\uc2ec AI \uac1c\ubc1c\uc758 \ud798<\/h3>\n<p><strong>1. \uae08\uc735\uad8c \uc0ac\uae30 \ud0d0\uc9c0 \uc2dc\uc2a4\ud15c \uac1c\uc120<\/strong><\/p>\n<p>\ud55c \uae08\uc735 \uae30\uad00\uc5d0\uc11c\ub294 AI\ub97c \ud65c\uc6a9\ud558\uc5ec \uc2e0\uc6a9\uce74\ub4dc \uac70\ub798\uc5d0\uc11c\uc758 \uc0ac\uae30 \uac70\ub798\ub97c \ud0d0\uc9c0\ud558\ub294 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud588\uc2b5\ub2c8\ub2e4. \ucd08\uae30\uc5d0\ub294 \ube60\ub978 \uac1c\ubc1c \uc18d\ub3c4\uc5d0 \uc9d1\uc911\ud558\uc5ec \ubaa8\ub378\uc744 \ubc30\ud3ec\ud588\uc9c0\ub9cc, \uc2e4\uc81c \uc6b4\uc601 \uacb0\uacfc \uc624\ud0d0(\uc815\uc0c1 \uac70\ub798\ub97c \uc0ac\uae30\ub85c \ud310\ub2e8)\uc774 \ub9ce\uc544 \uace0\uac1d \ubd88\ub9cc\uc774 \uc99d\uac00\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ubb38\uc81c\uc810:<\/strong> \uc8fc\ub85c &#8216;\uc815\ud655\ub3c4&#8217; \uc9c0\ud45c\uc5d0\ub9cc \uc9d1\uc911\ud558\uace0, &#8216;\uc815\ubc00\ub3c4&#8217;\uc640 &#8216;\uc7ac\ud604\uc728&#8217;\uc758 \uade0\ud615\uc744 \uace0\ub824\ud558\uc9c0 \uc54a\uc558\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud574\uacb0 \ubc29\uc548 (\ud3c9\uac00 \uc911\uc2ec \uc811\uadfc):<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>\ubaa9\ud45c \uc7ac\uc815\uc758:<\/strong> \uc0ac\uae30 \uac70\ub798 \ud0d0\uc9c0\uc728\uc744 \ub192\uc774\ub294 \ub3d9\uc2dc\uc5d0, \uc815\uc0c1 \uac70\ub798\ub97c \uc798\ubabb \ucc28\ub2e8\ud558\ub294 \ube44\uc728(\uc624\ud0d0)\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uac83\uc73c\ub85c \ubaa9\ud45c\ub97c \uc218\uc815\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud3c9\uac00 \uc9c0\ud45c \ubcc0\uacbd:<\/strong> \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\uc728\uc744 \ud568\uaed8 \uace0\ub824\ud558\ub294 F1-Score\uc640 \ud568\uaed8, \uc2e4\uc81c \ube44\uc988\ub2c8\uc2a4\uc5d0 \ubbf8\uce58\ub294 \uc601\ud5a5(\uace0\uac1d \ubd88\ud3b8, \uc190\uc2e4 \uae08\uc561)\uc744 \ubc18\uc601\ud558\ub294 \ub9de\ucda4\ud615 \uc9c0\ud45c\ub97c \ub3c4\uc785\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>A\/B \ud14c\uc2a4\ud2b8:<\/strong> \uc5ec\ub7ec \uac1c\uc120\ub41c \ubaa8\ub378 \ud6c4\ubcf4\uad70\uc744 \uc2e4\uc81c \uc6b4\uc601 \ud658\uacbd\uc758 \uc77c\ubd80 \uad6c\uac04\uc5d0 \uc801\uc6a9\ud558\uc5ec A\/B \ud14c\uc2a4\ud2b8\ub97c \uc9c4\ud589\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uacb0\uacfc:<\/strong> \uc624\ud0d0\ub960\uc744 15% \uc774\uc0c1 \ub0ae\ucd94\uba74\uc11c\ub3c4 \uc0ac\uae30 \uac70\ub798 \ud0d0\uc9c0\uc728\uc740 \uc720\uc9c0 \ub610\ub294 \uc18c\ud3ed \ud5a5\uc0c1\uc2dc\ucf1c \uace0\uac1d \ub9cc\uc871\ub3c4\ub97c \ub192\uc774\uace0 \uc2e4\uc81c \uae08\uc735 \uc190\uc2e4\uc744 \uc904\uc774\ub294 \ub370 \uc131\uacf5\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p><strong>2. \uc758\ub8cc \uc601\uc0c1 \uc9c4\ub2e8 \ubcf4\uc870 AI \uc815\ud655\ub3c4 \ud5a5\uc0c1<\/strong><\/p>\n<p>\uc758\ub8cc \uc601\uc0c1 \ubd84\uc11d AI \uac1c\ubc1c\uc5d0\uc11c\ub294 \ubbf8\uc138\ud55c \ucc28\uc774\ub97c \uac10\uc9c0\ud558\ub294 \uac83\uc774 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \ud55c \uc5f0\uad6c\ud300\uc740 \ud3d0\uc554 \uc9c4\ub2e8\uc744 \uc704\ud55c AI \ubaa8\ub378\uc744 \uac1c\ubc1c\ud588\uc2b5\ub2c8\ub2e4. \ucd08\uae30\uc5d0\ub294 \ub192\uc740 \uc815\ud655\ub3c4\ub97c \ub2ec\uc131\ud588\ub2e4\uace0 \ud310\ub2e8\ud588\uc9c0\ub9cc, \uc2e4\uc81c \uc784\uc0c1 \ud658\uacbd\uc5d0\uc11c \uc0ac\uc6a9\ud588\uc744 \ub54c \uc77c\ubd80 \ucd08\uae30 \ub2e8\uacc4\uc758 \uc554\uc744 \ub193\uce58\ub294 \uacbd\uc6b0\uac00 \ubc1c\uc0dd\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ubb38\uc81c\uc810:<\/strong> \ud559\uc2b5 \ub370\uc774\ud130\uc5d0 \ube44\ud574 \uc2e4\uc81c \uc784\uc0c1\uc5d0\uc11c \ub9c8\uc8fc\uce58\ub294 \ub2e4\uc591\ud55c \ud615\ud0dc\uc640 \ud06c\uae30\uc758 \uc554 \uc601\uc0c1\uc5d0 \ub300\ud55c \ucda9\ubd84\ud55c \uac80\uc99d\uc774 \uc774\ub8e8\uc5b4\uc9c0\uc9c0 \uc54a\uc558\uc2b5\ub2c8\ub2e4. &#8216;\uc804\uccb4 \uc815\ud655\ub3c4&#8217;\ub9cc\uc73c\ub85c\ub294 \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ubc1c\uacac\ud558\uae30 \uc5b4\ub824\uc6e0\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud574\uacb0 \ubc29\uc548 (\ud3c9\uac00 \uc911\uc2ec \uc811\uadfc):<\/strong><\/p>\n<\/li>\n<li>\n<p><strong>\uc138\ubd84\ud654\ub41c \ud3c9\uac00:<\/strong> \uc554\uc758 \ud06c\uae30, \uc704\uce58, \ud615\ud0dc \ub4f1 \ub2e4\uc591\ud55c \uae30\uc900\uc73c\ub85c \uc601\uc0c1\uc744 \uc138\ubd84\ud654\ud558\uc5ec \uac01 \uadf8\ub8f9\ubcc4\ub85c \uc7ac\ud604\uc728\uc744 \uce21\uc815\ud588\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub193\uce58\uae30 \uc26c\uc6b4 \uc791\uc740 \ud06c\uae30\uc758 \uc554\uc5d0 \ub300\ud55c \uc7ac\ud604\uc728\uc744 \uc9d1\uc911\uc801\uc73c\ub85c \ub192\uc774\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \uc0bc\uc558\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc804\ubb38\uac00 \uac80\ud1a0 \uac15\ud654:<\/strong> AI \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc758\ub8cc \uc804\ubb38\uac00\ub4e4\uc774 \uc9c1\uc811 \uac80\ud1a0\ud558\uace0 \ud53c\ub4dc\ubc31\uc744 \uc81c\uacf5\ud558\ub294 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud588\uc2b5\ub2c8\ub2e4. \uc774 \ud53c\ub4dc\ubc31\uc744 \ubc14\ud0d5\uc73c\ub85c \ubaa8\ub378\uc744 \uc9c0\uc18d\uc801\uc73c\ub85c \uac1c\uc120\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ubbfc\uac10\ub3c4 \ub192\uc740 \ub370\uc774\ud130\uc14b \uad6c\ucd95:<\/strong> \uc2e4\uc81c \uc784\uc0c1\uc5d0\uc11c \uc790\uc8fc \ubc1c\uc0dd\ud558\ub294 \uc608\uc678\uc801\uc778 \ucf00\uc774\uc2a4\ub4e4\uc744 \ud3ec\ud568\ud558\ub294 \ubcc4\ub3c4\uc758 \ud3c9\uac00 \ub370\uc774\ud130\uc14b\uc744 \uad6c\ucd95\ud558\uc5ec \ubaa8\ub378\uc758 \uac15\uac74\uc131(Robustness)\uc744 \ud14c\uc2a4\ud2b8\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uacb0\uacfc:<\/strong> \ucd08\uae30 \uc554 \ubc1c\uacac\uc728\uc744 10% \uc774\uc0c1 \ub192\uc600\uc73c\uba70, \uc624\uc9c4 \uac00\ub2a5\uc131\uc744 \uc904\uc5ec \uc758\ub8cc\uc9c4\uc758 \uc9c4\ub2e8 \uc815\ud655\ub3c4 \ud5a5\uc0c1\uc5d0 \ud06c\uac8c \uae30\uc5ec\ud588\uc2b5\ub2c8\ub2e4. \uc774\ub294 AI\uac00 \ub2e8\uc21c\ud55c \ub3c4\uad6c\ub97c \ub118\uc5b4 \uc2e4\uc81c \uc758\ub8cc \ud604\uc7a5\uc5d0\uc11c \uc2e0\ub8b0\ubc1b\ub294 \ud30c\ud2b8\ub108\uac00 \ub420 \uc218 \uc788\uc74c\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h3>\ud3c9\uac00 \uc911\uc2ec AI \uac1c\ubc1c \uc2dc \ud754\ud788 \uc800\uc9c0\ub974\ub294 \uc2e4\uc218\uc640 \uc8fc\uc758\uc0ac\ud56d<\/h3>\n<ul>\n<li>\n<p><strong>\uce21\uc815 \uac00\ub2a5\ud55c \ubaa9\ud45c \ubd80\uc7ac:<\/strong> &#8216;AI\ub97c \uc798 \ub9cc\ub4e4\uc790&#8217;\ub294 \ubaa8\ud638\ud55c \ubaa9\ud45c\ub294 \ud3c9\uac00 \uc911\uc2ec \uac1c\ubc1c\uc744 \uc5b4\ub835\uac8c \ub9cc\ub4ed\ub2c8\ub2e4. \ubc18\ub4dc\uc2dc \uad6c\uccb4\uc801\uc774\uace0 \uce21\uc815 \uac00\ub2a5\ud55c \ubaa9\ud45c\ub97c \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub2e8\uc77c \uc9c0\ud45c\uc5d0 \ub300\ud55c \uacfc\ub3c4\ud55c \uc758\uc874:<\/strong> \uc815\ud655\ub3c4 \ud558\ub098\ub9cc \ubcf4\uace0 \ubaa8\ub378\uc744 \ud310\ub2e8\ud558\uba74 \ub2e4\ub978 \uc911\uc694\ud55c \uce21\uba74\uc744 \ub193\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubb38\uc81c\uc758 \ud2b9\uc131\uc5d0 \ub9de\ub294 \ubcf5\ud569\uc801\uc778 \uc9c0\ud45c\ub97c \ud65c\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc758 \uc624\uc5fc:<\/strong> \ud559\uc2b5 \ub610\ub294 \uac80\uc99d \uacfc\uc815\uc5d0\uc11c \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uac00 \uc720\ucd9c\ub418\uba74 \ubaa8\ub378\uc758 \uc2e4\uc81c \uc131\ub2a5\uc744 \uacfc\ub300\ud3c9\uac00\ud558\uac8c \ub429\ub2c8\ub2e4. \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub294 \ubc18\ub4dc\uc2dc \ubd84\ub9ac\ud558\uc5ec \ucd5c\uc885 \ud3c9\uac00\uc5d0\ub9cc \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc2e4\uc81c \ud658\uacbd\uacfc\uc758 \uad34\ub9ac:<\/strong> \uc2e4\ud5d8\uc2e4 \ud658\uacbd\uc5d0\uc11c\uc758 \uc131\ub2a5\uc774 \uc2e4\uc81c \uc6b4\uc601 \ud658\uacbd\uc5d0\uc11c\uc758 \uc131\ub2a5\uacfc \ud56d\uc0c1 \uac19\uc9c0\ub294 \uc54a\uc2b5\ub2c8\ub2e4. \uac00\ub2a5\ud55c \uc2e4\uc81c \ud658\uacbd\uacfc \uc720\uc0ac\ud55c \uc870\uac74\uc5d0\uc11c \ud3c9\uac00\ud558\uac70\ub098, \ubc30\ud3ec \ud6c4 \uc9c0\uc18d\uc801\uc778 \ubaa8\ub2c8\ud130\ub9c1\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud3c9\uac00 \uacb0\uacfc\uc5d0 \ub300\ud55c \ubb34\uc2dc:<\/strong> \uc544\ubb34\ub9ac \uaf3c\uaf3c\ud558\uac8c \ud3c9\uac00\ud558\ub354\ub77c\ub3c4, \uadf8 \uacb0\uacfc\ub97c \ubc14\ud0d5\uc73c\ub85c \ubaa8\ub378\uc744 \uac1c\uc120\ud558\ub824\ub294 \ub178\ub825\uc774 \uc5c6\ub2e4\uba74 \ubb34\uc6a9\uc9c0\ubb3c\uc785\ub2c8\ub2e4. \ud3c9\uac00 \uacb0\uacfc\ub97c \uc801\uadf9\uc801\uc73c\ub85c \ud65c\uc6a9\ud558\uc5ec AI \ubaa8\ub378\uc744 \ubc1c\uc804\uc2dc\ucf1c\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h3>AI \uac1c\ubc1c\uc758 \ubbf8\ub798: \ud3c9\uac00\uc758 \uc911\uc694\uc131\uc740 \ub354\uc6b1 \ucee4\uc9c8 \uac83<\/h3>\n<p>AI \uae30\uc220\uc774 \ubc1c\uc804\ud558\uace0 \uc6b0\ub9ac \uc0b6\uc5d0 \ub354\uc6b1 \uae4a\uc219\uc774 \ud30c\uace0\ub4e4\uc218\ub85d, AI\uc758 \uc131\ub2a5\uacfc \uc548\uc804\uc131\uc744 \uac80\uc99d\ud558\ub294 &#8216;\ud3c9\uac00&#8217;\uc758 \uc911\uc694\uc131\uc740 \ub354\uc6b1 \ucee4\uc9c8 \uac83\uc785\ub2c8\ub2e4. \ub2e8\uc21c\ud788 \ucd5c\uc2e0 \uae30\uc220\uc744 \ube60\ub974\uac8c \ub3c4\uc785\ud558\ub294 \uac83\uc744 \ub118\uc5b4, AI\uac00 \uc2e4\uc81c\ub85c \uc5b4\ub5a4 \uac00\uce58\ub97c \ucc3d\ucd9c\ud558\uace0 \uc5b4\ub5a4 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294\uc9c0 \uc81c\ub300\ub85c \uc774\ud574\ud558\uace0 \uce21\uc815\ud558\ub294 &#8216;\ud3c9\uac00 \uc911\uc2ec AI \uac1c\ubc1c&#8217;\uc740 \uc774\uc81c \uc120\ud0dd\uc774 \uc544\ub2cc \ud544\uc218\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>\uacb0\ub860<\/h3>\n<p>AI \uac1c\ubc1c\uc5d0\uc11c &#8216;\uc81c\ub300\ub85c \uce21\uc815\ud558\ub294 \uac83&#8217;\uc740 \ub2e8\uc21c\ud788 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud655\uc778\ud558\ub294 \uac83\uc744 \ub118\uc5b4, AI\uac00 \uc2e4\uc81c\ub85c \ube44\uc988\ub2c8\uc2a4 \ubaa9\ud45c\ub97c \ub2ec\uc131\ud558\uace0 \uc0ac\ud68c\uc5d0 \uae0d\uc815\uc801\uc778 \uc601\ud5a5\uc744 \ubbf8\uce58\ub3c4\ub85d \ubcf4\uc7a5\ud558\ub294 \ud575\uc2ec \uacfc\uc815\uc785\ub2c8\ub2e4. \uba85\ud655\ud55c \ubaa9\ud45c \uc124\uc815, \uc801\uc808\ud55c \ud3c9\uac00 \uc9c0\ud45c \uc120\ud0dd, \ucca0\uc800\ud55c \ub370\uc774\ud130 \uad00\ub9ac, \uadf8\ub9ac\uace0 \uc9c0\uc18d\uc801\uc778 \ubaa8\ub2c8\ud130\ub9c1\uc744 \ud1b5\ud574 \ud3c9\uac00 \uc911\uc2ec AI \uac1c\ubc1c\uc744 \uc2e4\ucc9c\ud55c\ub2e4\uba74, \uc18d\ub3c4 \uacbd\uc7c1\uc5d0\uc11c \ubc97\uc5b4\ub098 \uc9c4\uc815\ud55c AI \uc131\uacf5\uc744 \uac70\ub458 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\uc2e4\ud589 \uc561\uc158 1:<\/strong> \ud604\uc7ac \uc9c4\ud589 \uc911\uc778 AI \ud504\ub85c\uc81d\ud2b8\uc758 \ubaa9\ud45c\ub97c \uad6c\uccb4\uc801\uc774\uace0 \uce21\uc815 \uac00\ub2a5\ud55c KPI\ub85c \uc7ac\uc815\uc758\ud574\ubcf4\uc138\uc694.<\/p>\n<\/li>\n<li>\n<p><strong>\uc2e4\ud589 \uc561\uc158 2:<\/strong> \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc0ac\uc6a9\ub418\ub294 \ud3c9\uac00 \uc9c0\ud45c\uac00 \ube44\uc988\ub2c8\uc2a4 \ubaa9\ud45c\uc640 \uc798 \ubd80\ud569\ud558\ub294\uc9c0 \uc810\uac80\ud558\uace0, \ud544\uc694\ud558\ub2e4\uba74 \uc0c8\ub85c\uc6b4 \uc9c0\ud45c\ub97c \ucd94\uac00\ud558\uc138\uc694.<\/p>\n<\/li>\n<li>\n<p><strong>\uc2e4\ud589 \uc561\uc158 3:<\/strong> AI \ubaa8\ub378 \ubc30\ud3ec \ud6c4 \uc131\ub2a5 \uc800\ud558\ub97c \uac10\uc9c0\ud558\uace0 \ub300\uc751\ud558\uae30 \uc704\ud55c \ubaa8\ub2c8\ud130\ub9c1 \ubc0f \uc7ac\ud3c9\uac00 \uacc4\ud68d\uc744 \uc218\ub9bd\ud558\uc138\uc694.<\/p>\n<\/li>\n<\/ul>\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:\/\/ai.googleblog.com\/2021\/04\/evaluating-machine-learning-models-for.html\" target=\"_blank\" rel=\"noopener noreferrer\">Google AI Blog &#8211; Model Evaluation<\/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\">Rediscovering Evaluation in AI Development: Why It Matters More Than Speed<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">As competition in AI development intensifies, many people are focusing on one question: <strong>How fast can we build it?<\/strong> But many experts now stress that speed matters less than <strong>building it correctly<\/strong>\u2014in other words, accurately measuring the performance and value of AI. That is why the concept of <strong>evaluation-driven AI development<\/strong> is gaining attention.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why Is Measuring Properly More Important Than Simply Building Well?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Developing an AI model involves much more than writing code and implementing algorithms. AI must solve complex real-world problems and create tangible value. To achieve that, it is essential to evaluate objectively how well the model aligns with actual business goals and whether it produces any unintended side effects.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Verifying Goal Achievement<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If an AI model is built to solve a specific problem, then it must be measured on how effectively it solves that problem. For example, if the AI is for autonomous driving, it should be measured on how safely it drives and how efficiently it plans routes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Preventing Waste of Resources<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Investing large amounts of time and money into an AI model whose performance has not been properly validated is wasteful. A structured evaluation process helps identify issues early and prevents unnecessary resource spending.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ensuring Reliability and Safety<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI affects many parts of daily life. If an AI system makes biased or incorrect decisions, the consequences can be serious. That makes evaluation for reliability and safety critically important.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Enabling Continuous Improvement<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI models are not built once and finished. They must continuously collect data in real-world settings, monitor performance, and improve over time. An effective evaluation framework is the foundation for this ongoing improvement.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How Should Evaluation-Driven AI Development Begin?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Evaluation-driven AI development can be approached systematically through the following stages.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. Set Clear Goals and Define KPIs<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The first step is to define a specific goal for what the AI model is supposed to achieve. That goal should be measurable and directly tied to business objectives.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Examples<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Goal:<\/strong> Improve customer satisfaction with a customer-service chatbot by 20%<br><strong>KPIs:<\/strong> Customer satisfaction score, inquiry resolution time, repeat inquiry rate<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Goal:<\/strong> Reduce defect rate in a manufacturing process by 15%<br><strong>KPIs:<\/strong> Defect detection accuracy, false positive rate, inspection time<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Choose the Right Evaluation Metrics and Methodologies<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Once goals and KPIs are defined, the next step is to decide how the model\u2019s performance should be measured. It is not enough to look only at <strong>accuracy<\/strong>. Different problems require different metrics.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Key Evaluation Metrics<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Accuracy:<\/strong><br>The proportion of total predictions that were correct. Commonly used in classification tasks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Precision:<\/strong><br>Of all the items the model predicted as positive, how many were actually positive. Important when reducing false positives matters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Recall:<\/strong><br>Of all the actual positive items, how many the model correctly identified as positive. Important when reducing false negatives matters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>F1-Score:<\/strong><br>The harmonic mean of precision and recall. Useful when both are important.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>ROC Curve and AUC:<\/strong><br>Used to evaluate binary classification performance more broadly across multiple thresholds.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error):<\/strong><br>Used in regression tasks to measure the difference between predictions and actual values.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Evaluation Methodologies<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Cross-Validation:<\/strong><br>The dataset is divided into multiple folds, and training and evaluation are repeated across them to improve generalization.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>A\/B Testing:<\/strong><br>Two or more models or versions are compared in a real user environment to see which performs better.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Simulation:<\/strong><br>The model is tested in conditions similar to the real world in order to estimate performance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Prepare and Manage the Dataset<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The reliability of evaluation depends heavily on the quality of the data being used.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Training Data:<\/strong><br>Used to train the model.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Validation Data:<\/strong><br>Used during training to monitor performance and tune hyperparameters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Test Data:<\/strong><br>Used to evaluate the final model objectively. This data should never be used during training or validation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data Quality Management:<\/strong><br>Bias, noise, and missing values must all be carefully managed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Monitor Continuously and Re-Evaluate Regularly<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Even after deployment, an AI model\u2019s performance can degrade over time. Changes in data distribution (<strong>data drift<\/strong>) or changes in the nature of the problem (<strong>concept drift<\/strong>) may cause the model\u2019s predictions to become less aligned with reality.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Real-Time Monitoring:<\/strong><br>Track predictions and shifts in input data characteristics continuously.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Regular Re-Evaluation:<\/strong><br>Use recent data to re-evaluate model performance periodically, and retrain or update the model if necessary.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Real Success Stories: The Power of Evaluation-Driven AI Development<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">1. Improving Fraud Detection in the Financial Sector<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A financial institution developed an AI system to detect fraudulent credit-card transactions. At first, the team focused heavily on deploying quickly. But in real operation, the system generated too many false positives\u2014legitimate transactions flagged as fraud\u2014which led to customer complaints.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Problem:<\/strong><br>The team focused mostly on <strong>accuracy<\/strong> and did not properly consider the balance between <strong>precision<\/strong> and <strong>recall<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Solution through an evaluation-driven approach:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Redefined the goal:<\/strong> Not only to detect fraud more effectively, but also to reduce false positives.<\/li>\n\n\n\n<li><strong>Changed evaluation metrics:<\/strong> Introduced F1-score and business-specific metrics that reflected customer inconvenience and financial impact.<\/li>\n\n\n\n<li><strong>Used A\/B testing:<\/strong> Tested several improved model candidates in part of the real operational environment.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Result:<\/strong><br>The institution reduced the false positive rate by more than 15% while maintaining or slightly improving fraud detection. This improved customer satisfaction and reduced real financial losses.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Improving the Accuracy of AI for Medical Imaging Support<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In medical imaging AI, detecting subtle differences is critically important. One research team developed an AI model for lung cancer diagnosis. At first, the model appeared to have high accuracy, but in clinical use it sometimes failed to detect early-stage cancers.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Problem:<\/strong><br>The evaluation process did not sufficiently validate the wide range of shapes and sizes of tumors encountered in real clinical settings. Overall accuracy alone failed to reveal this weakness.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Solution through an evaluation-driven approach:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Introduced more granular evaluation:<\/strong> Measured recall separately for different categories of cancer size, location, and shape. Special emphasis was placed on improving recall for small, easily missed tumors.<\/li>\n\n\n\n<li><strong>Strengthened expert review:<\/strong> Built a system in which medical professionals directly reviewed the model\u2019s predictions and provided feedback.<\/li>\n\n\n\n<li><strong>Built a high-sensitivity evaluation dataset:<\/strong> Created a separate test set containing exceptional cases that occur frequently in real clinical environments in order to test robustness.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Result:<\/strong><br>The early cancer detection rate increased by more than 10%, and the risk of misdiagnosis fell. This significantly improved diagnostic support for clinicians and showed that AI could become a trusted partner in real healthcare settings.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Common Mistakes and Precautions in Evaluation-Driven AI Development<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Lack of Measurable Goals<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A vague goal such as \u201cLet\u2019s build a good AI\u201d makes evaluation-driven development almost impossible. Goals must always be specific and measurable.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Over-Reliance on a Single Metric<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Judging a model only by accuracy can cause important weaknesses to be overlooked. Multiple metrics appropriate to the problem should be used together.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Contamination of Test Data<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If test data leaks into training or validation, the model\u2019s actual performance will be overestimated. Test data must be kept completely separate and used only for final evaluation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Gap Between Lab Conditions and Real Environments<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Good performance in a laboratory setting does not always translate into good performance in production. Evaluation should be conducted under conditions as close as possible to reality, and ongoing monitoring after deployment is essential.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ignoring Evaluation Results<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No matter how carefully evaluation is performed, it is useless if the results are not used to improve the model. Evaluation should always feed back into model refinement.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Future of AI Development: Evaluation Will Matter Even More<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">As AI becomes more advanced and more deeply integrated into daily life, the importance of <strong>evaluation<\/strong>\u2014verifying performance and safety\u2014will continue to grow. It is no longer enough simply to adopt the latest technology quickly. Understanding and measuring the real value and impact of AI has become essential. Evaluation-driven AI development is no longer optional; it is a necessity.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In AI development, <strong>measuring properly<\/strong> is not just about checking model performance. It is a core process that ensures AI actually achieves business goals and creates positive social impact. By setting clear goals, selecting appropriate evaluation metrics, managing data carefully, and monitoring performance continuously, organizations can practice evaluation-driven AI development and achieve real AI success instead of merely racing for speed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Action Step 1<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Redefine the goal of any current AI project into specific, measurable KPIs.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Action Step 2<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Check whether the evaluation metrics being used actually align with business goals, and add new metrics if necessary.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Action Step 3<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Build a monitoring and re-evaluation plan so that model performance decline can be detected and addressed after deployment.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI \uac1c\ubc1c\uc5d0\uc11c \uc18d\ub3c4 \uacbd\uc7c1\uc740 \uce58\uc5f4\ud558\uc9c0\ub9cc, \uacb0\uad6d \uc81c\ub300\ub85c \ub41c \ud3c9\uac00 \uc5c6\uc774\ub294 \uc131\uacf5\uc744 \uc7a5\ub2f4\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4. \ud3c9\uac00 \uc911\uc2ec AI \uac1c\ubc1c\uc740 \ub2e8\uc21c\ud788 \ube60\ub974\uac8c \ub9cc\ub4dc\ub294 \uac83\uc744 \ub118\uc5b4, \uba85\ud655\ud55c \ubaa9\ud45c\uc640 \uce21\uc815 \uc9c0\ud45c\ub97c \ud1b5\ud574 AI\uc758 \uc2e4\uc81c \uc131\ub2a5\uacfc \uac00\uce58\ub97c \uadf9\ub300\ud654\ud558\ub294 \ubc29\ubc95\ub860\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uc65c \ud3c9\uac00\uac00 \uc911\uc694\ud55c\uc9c0, \uc5b4\ub5bb\uac8c \ud6a8\uacfc\uc801\uc778 \ud3c9\uac00\ub97c \uc218\ud589\ud560 \uc218 \uc788\ub294\uc9c0, \uadf8\ub9ac\uace0 \uc2e4\uc81c \uc131\uacf5 \uc0ac\ub840\ub97c \ud1b5\ud574 \uadf8 \uc6d0\ub9ac\ub97c \ud30c\ud5e4\uce69\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":[436,441,437,438,440,439,303,302,305,301,306,307,442,304,300],"class_list":["post-105","post","type-post","status-publish","format-standard","hentry","category-ai","tag-ai-development-methodology","tag-ai-model-evaluation","tag-ai-performance-measurement","tag-ai-reliability","tag-ai-safety","tag-ai-success-stories","tag-ai--","tag-ai-","tag-machine-learning-evaluation","tag-304","tag---ai-"],"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\/105","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=105"}],"version-history":[{"count":1,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/105\/revisions"}],"predecessor-version":[{"id":128,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/105\/revisions\/128"}],"wp:attachment":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=105"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=105"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=105"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}