½ÃÀ庸°í¼­
»óǰÄÚµå
1293834

µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¼ºÀå, ¹Ì·¡ Àü¸Á, °æÀï ºÐ¼®(2023-2031³â)

Data Annotation Tools Market - Growth, Future Prospects and Competitive Analysis, 2023 - 2031

¹ßÇàÀÏ: | ¸®¼­Ä¡»ç: Acute Market Reports | ÆäÀÌÁö Á¤º¸: ¿µ¹® 115 Pages | ¹è¼Û¾È³» : 1-2ÀÏ (¿µ¾÷ÀÏ ±âÁØ)

    
    
    



¡Ø º» »óǰÀº ¿µ¹® ÀÚ·á·Î Çѱ۰ú ¿µ¹® ¸ñÂ÷¿¡ ºÒÀÏÄ¡ÇÏ´Â ³»¿ëÀÌ ÀÖÀ» °æ¿ì ¿µ¹®À» ¿ì¼±ÇÕ´Ï´Ù. Á¤È®ÇÑ °ËÅ並 À§ÇØ ¿µ¹® ¸ñÂ÷¸¦ Âü°íÇØÁֽñ⠹ٶø´Ï´Ù.

µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀåÀº 2023-2031³âÀÇ ¿¹Ãø ±â°£ Áß 25%ÀÇ CAGRÀ» ³ªÅ¸³¾ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù. ÀÌ ½ÃÀåÀº ÀΰøÁö´É(AI), ¸Ó½Å·¯´×(ML), ÄÄÇ»ÅÍ ºñÀü µî ´Ù¾çÇÑ »ê¾÷¿¡¼­ ÁÖ¼®ÀÌ ´Þ¸° µ¥ÀÌÅÍ¿¡ ´ëÇÑ ¼ö¿ä°¡ Áõ°¡ÇÔ¿¡ µû¶ó ÃÖ±Ù ¸î ³âµ¿¾È Å« ¼ºÀå¼¼¸¦ º¸À̰í ÀÖ½À´Ï´Ù. µ¥ÀÌÅÍ ¾î³ëÅ×À̼ÇÀº AI ¹× ML ¾Ë°í¸®ÁòÀÌ ÀÌÇØÇϱ⠽±µµ·Ï µ¥ÀÌÅÍ¿¡ ¶óº§À» ºÙÀ̰í ű׸¦ ºÙÀÌ´Â °úÁ¤À» ¸»ÇÕ´Ï´Ù. ÀÌ·¯ÇÑ ÅøÀº °íǰÁúÀÇ ÁÖ¼® µ¥ÀÌÅÍ ¼¼Æ®¸¦ Á¦°øÇÔÀ¸·Î½á AI ¸ðµ¨ ÇнÀ°ú Á¤È®µµ Çâ»ó¿¡ Áß¿äÇÑ ¿ªÇÒÀ» ÇÕ´Ï´Ù. µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀåÀÇ ¼ºÀå¿¡ ±â¿©ÇÏ´Â ÁÖ¿ä ¿äÀÎ Áß Çϳª´Â AI ±â¼úÀÇ ±Þ¼ÓÇÑ ¹ßÀüÀ¸·Î, AI ¿ëµµÀÌ »ê¾÷ Àü¹Ý¿¡ °ÉÃÄ ³Î¸® º¸±ÞµÊ¿¡ µû¶ó ÀÌ·¯ÇÑ ¸ðµ¨À» ÈÆ·ÃÇϱâ À§ÇÑ ¾î³ëÅ×ÀÌ¼Ç µ¥ÀÌÅÍ¿¡ ´ëÇÑ ¿ä±¸°¡ ±ÞÁõÇϰí ÀÖ½À´Ï´Ù. µ¥ÀÌÅÍ ÁÖ¼® ÅøÀº Á¶Á÷ÀÌ ´ë·®ÀÇ µ¥ÀÌÅÍ¿¡ ÁÖ¼®À» ´Þ ¼ö ÀÖ´Â È¿À²ÀûÀ̰í È®Àå °¡´ÉÇÑ ¼Ö·ç¼ÇÀ» Á¦°øÇÏ¿© ¼öµ¿À¸·Î ÁÖ¼®À» ´Ù´Â ¹æ¹ý¿¡ ºñÇØ ½Ã°£°ú ³ëµ¿·ÂÀ» Àý¾àÇÒ ¼ö ÀÖµµ·Ï µµ¿ÍÁÝ´Ï´Ù. ¶ÇÇÑ ºòµ¥ÀÌÅÍÀÇ °¡¿ë¼º Áõ°¡¿Í Ŭ¶ó¿ìµå ÄÄÇ»ÆÃÀÇ µµÀÔÀÌ È®´ëµÇ¸é¼­ µ¥ÀÌÅÍ ÁÖ¼® Åø¿¡ ´ëÇÑ ¼ö¿ä°¡ ´õ¿í Áõ°¡Çϰí ÀÖ½À´Ï´Ù. µðÁöÅÐ ÄÁÅÙÃ÷ÀÇ º¸±Þ°ú ÀÎÅÍ³Ý ¿¬°á ±â±â Áõ°¡·Î ÀÎÇØ ¹æ´ëÇÑ ¾çÀÇ ºñÁ¤Çü µ¥ÀÌÅͰ¡ Á¸ÀçÇϸç, ÀÇ¹Ì ÀÖ´Â ÀλçÀÌÆ®¸¦ µµÃâÇϱâ À§Çؼ­´Â ÁÖ¼®À» ´Þ¾Æ¾ß ÇÕ´Ï´Ù. Ŭ¶ó¿ìµå ±â¹Ý µ¥ÀÌÅÍ ÁÖ¼® ÅøÀº À¯¿¬¼º, Á¢±Ù¼º, Çù¾÷ ±â´ÉÀ» °®Ãß°í ÀÖÀ¸¸ç, ¸ðµç ±Ô¸ðÀÇ Á¶Á÷¿¡¼­ ³ôÀº Æò°¡¸¦ ¹Þ°í ÀÖ½À´Ï´Ù.

ÀΰøÁö´É(AI) ¹× ¸Ó½Å·¯´×(ML) ±â¼ú äÅà Áõ°¡

AI ¹× ML ±â¼úÀÌ »ê¾÷ Àü¹Ý¿¡ ºü¸£°Ô µµÀԵǸ鼭 µ¥ÀÌÅÍ ÁÖ¼® Åø ½ÃÀåÀÇ ÁÖ¿ä ÃËÁø¿äÀÎÀ¸·Î ÀÛ¿ëÇϰí ÀÖÀ¸¸ç, AI ¹× ML ¾Ë°í¸®ÁòÀº ÈÆ·Ã°ú Á¤È®µµ Çâ»óÀ» À§ÇØ ÁÖ¼® µ¥ÀÌÅÍ¿¡ Å©°Ô ÀÇÁ¸Çϰí ÀÖ½À´Ï´Ù. Á¶Á÷ÀÌ ¾÷¹« È¿À²¼ºÀ» ³ôÀÌ°í °æÀï ¿ìÀ§¸¦ È®º¸Çϱâ À§ÇØ AI¿Í MLÀÇ °¡Ä¡¸¦ ÀνÄÇÔ¿¡ µû¶ó µ¥ÀÌÅÍ ÁÖ¼® Åø¿¡ ´ëÇÑ ¼ö¿ä°¡ ±ÞÁõÇϰí ÀÖÀ¸¸ç, ±¸±Û, ¾Æ¸¶Á¸, ¸¶ÀÌÅ©·Î¼ÒÇÁÆ®¿Í °°Àº ±â¾÷µéÀº AI¿Í ML ¿¬±¸ ¹× °³¹ß¿¡ ¸¹Àº ÅõÀÚ¸¦ Çϰí ÀÖ½À´Ï´Ù. À̵éÀº ÀÚ»ç Á¦Ç° ¹× ¼­ºñ½º¿¡ AI ±â´ÉÀ» ÅëÇÕÇϰí ÀÖÀ¸¸ç, À̸¦ À§ÇØ ´ë·®ÀÇ ÁÖ¼® µ¥ÀÌÅͰ¡ ÇÊ¿äÇÕ´Ï´Ù. ÀÌ¿¡ µû¶ó À̵éÀÇ AI ³ë·ÂÀ» Áö¿øÇÏ´Â µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø¿¡ ´ëÇÑ ¼ö¿ä°¡ Áõ°¡Çϰí ÀÖ½À´Ï´Ù.

°íǰÁú ¾î³ëÅ×ÀÌ¼Ç µ¥ÀÌÅͼ¼Æ®¿¡ ´ëÇÑ ¿ä±¸ Áõ°¡

AI ¹× ML ÇÁ·ÎÁ§Æ®ÀÇ ¼º°øÀ» À§Çؼ­´Â °íǰÁú ÁÖ¼® µ¥ÀÌÅÍ ¼¼Æ®ÀÇ Çʿ伺ÀÌ ¸Å¿ì Áß¿äÇØÁö°í ÀÖ½À´Ï´Ù. ÁÖ¼® µ¥ÀÌÅÍ´Â ¾Ë°í¸®ÁòÀ» È¿°úÀûÀ¸·Î ÇнÀ½ÃŰ´Â µ¥ ÇÊ¿äÇÑ ÄÁÅØ½ºÆ®¿Í ¶óº§À» Á¦°øÇÕ´Ï´Ù. Á¤È®ÇÏ°í ½Å·ÚÇÒ ¼ö ÀÖ´Â AI ¸ðµ¨À» ¸ñÇ¥·Î ÇÏ´Â ±â¾÷µéÀº °íǰÁú ¾î³ëÅ×À̼ÇÀ» »ý¼ºÇÒ ¼ö ÀÖ´Â µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø¿¡ ´ëÇÑ ¼ö¿ä°¡ Áõ°¡Çϰí ÀÖ½À´Ï´Ù. ÁÖ¼®ÀÇ Ç°ÁúÀº ÀÌ·¯ÇÑ ¸ðµ¨ÀÇ ¼º´É°ú ½Å·Ú¼º¿¡ Á÷Á¢ÀûÀÎ ¿µÇâÀ» ¹ÌĨ´Ï´Ù. Á¶Á÷Àº ÁÖ¼® µ¥ÀÌÅÍ ¼¼Æ®ÀÇ Á¤È®¼º°ú Àϰü¼ºÀ» º¸ÀåÇϱâ À§ÇØ µ¥ÀÌÅÍ ÁÖ¼® Åø¿¡ ÅõÀÚÇϰí ÀÖ½À´Ï´Ù.

ÁÖ¼® µ¥ÀÌÅ͸¦ ÇÊ¿ä·Î ÇÏ´Â »ê¾÷ÀÇ È®´ë

ÁÖ¼® µ¥ÀÌÅÍÀÇ È°¿ëÀº ÇÑ »ê¾÷¿¡ ±¹ÇÑµÈ °ÍÀÌ ¾Æ´Õ´Ï´Ù. ÇコÄɾî, ÀÚµ¿Â÷, À¯Åë, ±ÝÀ¶ µî ´Ù¾çÇÑ ºÐ¾ß¿¡¼­ AI¿Í ML ±â¼úÀ» Ȱ¿ëÇϰí ÀÖ½À´Ï´Ù. ÀÌµé »ê¾÷¿¡¼­´Â È¿À²ÀûÀÎ ¸ðµ¨ ÇнÀÀ» À§ÇØ °¢ µµ¸ÞÀο¡ ƯȭµÈ ¾î³ëÅ×ÀÌ¼Ç µ¥ÀÌÅ͸¦ ÇÊ¿ä·Î Çϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ÁÖ¼® µ¥ÀÌÅ͸¦ ÇÊ¿ä·Î ÇÏ´Â »ê¾÷ÀÇ È®´ë°¡ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀåÀÇ ¼ºÀå¿¡ ±â¿©Çϰí ÀÖ½À´Ï´Ù. ´Ù¾çÇÑ »ê¾÷ ºÐ¾ßÀÇ ¼ö¸¹Àº »ç·Ê ¿¬±¸¿Í ¼º°ø »ç·Ê´Â AI¿Í ML ±â¼úÀÇ Àû¿ëÀ» ¼Ò°³Çß½À´Ï´Ù. ¿¹¸¦ µé¾î ÀÇ·á ºÐ¾ß¿¡¼­´Â ÁÖ¼®ÀÌ ´Þ¸° ÀÇ·á ¿µ»óÀÌ Áø´Ü ¹× Ä¡·á °èȹ ¼ö¸³À» À§ÇÑ ¾Ë°í¸®Áò ÇнÀ¿¡ Ȱ¿ëµÇ°í ÀÖ½À´Ï´Ù. ÀÚÀ²ÁÖÇàÂ÷¿¡¼­´Â ¹°Ã¼¸¦ °¨ÁöÇϰí ÀνÄÇÏ´Â µ¥ ÀÖÀ¸¸ç, ÁÖ¼® µ¥ÀÌÅͰ¡ ÇʼöÀûÀÔ´Ï´Ù. ÀÌó·³ ´Ù¾çÇÑ »ê¾÷¿¡¼­ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç ÅøÀÌ ÇÊ¿äÇÏ´Ù´Â °ÍÀ» ¾Ë ¼ö ÀÖ½À´Ï´Ù.

°³ÀÎÁ¤º¸ º¸È£¿Í À±¸®Àû ¹®Á¦

µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀåÀº °³ÀÎ Á¤º¸ Ãë±Þ¿¡ µû¸¥ ÇÁ¶óÀ̹ö½Ã ¹× À±¸®Àû ¹®Á¦·Î ÀÎÇØ Å« Á¦¾à ¿äÀο¡ Á÷¸éÇØ ÀÖ½À´Ï´Ù. µ¥ÀÌÅÍ ÁÖ¼® ´Þ±â¿¡´Â °³ÀÎ ½Äº° Á¤º¸(PII), ÀÇ·á ±â·Ï, À繫 µ¥ÀÌÅÍ µî ¹Î°¨ÇÑ Á¤º¸¸¦ ´Ù·ç´Â °æ¿ì°¡ ¸¹½À´Ï´Ù. Á¶Á÷Àº °³ÀÎÀÇ ÇÁ¶óÀ̹ö½Ã ±Ç¸®¸¦ º¸È£Çϱâ À§ÇØ ÇÁ¶óÀ̹ö½Ã °ü·Ã ±ÔÁ¤°ú À±¸®Àû °¡À̵å¶óÀÎÀ» ÁؼöÇØ¾ß ÇÕ´Ï´Ù. ÀÌ·¯ÇÑ ¹®Á¦¸¦ ÇØ°áÇÏÁö ¸øÇÏ¸é ¹ýÀû ó¹ú, ÆòÆÇ ¼Õ»ó ¹× °í°´ ½Å·Ú »ó½Ç·Î À̾îÁú ¼ö ÀÖ½À´Ï´Ù. ÃÖ±Ù µ¥ÀÌÅÍ À¯Ãâ ¹× °³ÀÎ Á¤º¸ À¯Ãâ »ç°ÇÀ¸·Î ÀÎÇØ µ¥ÀÌÅÍ ÇÁ¶óÀ̹ö½Ã¿¡ ´ëÇÑ »çȸÀû Àνİú ±ÔÁ¦¿¡ ´ëÇÑ °¨½Ã°¡ °­È­µÇ°í ÀÖ½À´Ï´Ù. À¯·´¿¬ÇÕ(EU)ÀÇ ÀϹݰ³ÀÎÁ¤º¸º¸È£¹ý(GDPR)°ú ¼¼°è °¢±¹ÀÇ À¯»çÇÑ µ¥ÀÌÅÍ º¸È£¹ýÀº °³ÀÎ µ¥ÀÌÅÍÀÇ ¼öÁý, ó¸®, ÀúÀå¿¡ ´ëÇÑ ¾ö°ÝÇÑ ¿ä°ÇÀ» ºÎ°úÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ±ÔÁ¦¸¦ À§¹ÝÇÒ °æ¿ì, ¾ö°ÝÇÑ ¹úÄ¢°ú ¹ú±ÝÀÌ ºÎ°úµÉ ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ ¾ó±¼ ÀνÄÀ̳ª »ýü Á¤º¸¿Í °°Àº ¹Î°¨ÇÑ µ¥ÀÌÅÍ »ç¿ë¿¡ ´ëÇÑ À±¸®Àû °í·Á´Â Ã¥ÀÓ°¨ ÀÖ´Â AI »ç¿ëÀ» À§ÇÑ ³íÀÇ¿Í ¿ä±¸¸¦ ºÒ·¯ÀÏÀ¸Å°°í ÀÖ½À´Ï´Ù. µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå¿¡¼­ Ȱµ¿ÇÏ´Â Á¶Á÷Àº °­·ÂÇÑ µ¥ÀÌÅÍ º¸È£ Á¶Ä¡¸¦ ½ÃÇàÇϰí, »çÀü µ¿ÀǸ¦ È®º¸Çϰí, ÇÁ¶óÀ̹ö½Ã ¹ÙÀÌ µðÀÚÀÎ ¿øÄ¢À» äÅÃÇÏ¿© ÇÁ¶óÀ̹ö½Ã¿Í À±¸®Àû °í·Á¸¦ ¿ì¼±½ÃÇØ¾ß ÇÕ´Ï´Ù. µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç ÅøÀÌ Áö¼ÓÀûÀ¸·Î ¼ºÀåÇÏ°í ½ÃÀå¿¡¼­ ÀÎÁ¤¹Þ±â À§Çؼ­´Â ÀÌ·¯ÇÑ ¿ì·Á¸¦ ÇØ°áÇϰí Ã¥ÀÓ°¨ ÀÖ´Â µ¥ÀÌÅÍ Ãë±Þ¿¡ ´ëÇÑ ¾à¼ÓÀ» º¸¿©ÁÖ´Â °ÍÀÌ ÇʼöÀûÀÔ´Ï´Ù.

ÅØ½ºÆ® ¾î³ëÅ×ÀÌ¼Ç Åø ºÎ¹®ÀÌ ¸ÅÃâ °ßÀÎ

µ¥ÀÌÅÍ ÁÖ¼® Åø ½ÃÀåÀº ÅØ½ºÆ®, À̹ÌÁö, À½¼º µî ÁÖ¼® À¯Çü¿¡ µû¶ó ºÐ·ùµË´Ï´Ù. ÀÌ Áß °¡Àå ³ôÀº CAGR(2023-2031³â)À» ±â·ÏÇÒ °ÍÀ¸·Î ¿¹»óµÇ´Â ºÎ¹®Àº À̹ÌÁö ¾î³ëÅ×ÀÌ¼Ç Åø ºÎ¹®ÀÔ´Ï´Ù. ÀÚÀ²ÁÖÇàÂ÷, ¼Ò¸Å, ÇコÄɾî, °¨½Ã µî ´Ù¾çÇÑ »ê¾÷¿¡¼­ ÄÄÇ»ÅÍ ºñÀü ±â¼ú äÅÃÀÌ Áõ°¡ÇÔ¿¡ µû¶ó À̹ÌÁö ¾î³ëÅ×ÀÌ¼Ç Åø¿¡ ´ëÇÑ ¼ö¿ä°¡ Å©°Ô Áõ°¡Çϰí ÀÖ½À´Ï´Ù. À̹ÌÁö ¾î³ëÅ×À̼ÇÀº À̹ÌÁö ÀÎ½Ä ¹× ¹°Ã¼ °¨Áö ÀÛ¾÷¿¡¼­ AI ¸ðµ¨À» ÈÆ·Ã½Ã۱â À§ÇØ °´Ã¼, °ü½É ¿µ¿ª, °æ°è »óÀÚ, ½Ã¸Çƽ ¼¼ºÐÈ­ µîÀÇ ¶óº§¸µÀ» Æ÷ÇÔÇÕ´Ï´Ù. À̹ÌÁö µ¥ÀÌÅÍÀÇ º¹ÀâÇÑ Æ¯¼º°ú Á¤È®ÇÏ°í »ó¼¼ÇÑ ÁÖ¼®ÀÇ Çʿ伺ÀÌ ÀÌ ºÎ¹®ÀÇ ³ôÀº ¼ºÀå·ü¿¡ ±â¿©Çϰí ÀÖ½À´Ï´Ù. ÇÑÆí, ¸ÅÃâ Ãø¸é¿¡¼­´Â ÅØ½ºÆ® ÁÖ¼® Åø ºÎ¹®ÀÌ 2022³â °¡Àå ³ôÀº Á¡À¯À²À» Â÷ÁöÇÒ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù. ÅØ½ºÆ® ÁÖ¼®Àº ÀÚ¿¬¾î ó¸®(NLP) ¿ëµµ, °¨Á¤ ºÐ¼®, ÅØ½ºÆ® ºÐ·ù, ¾ð¾î ¹ø¿ª¿¡ ÇʼöÀûÀÔ´Ï´Ù. 꺿, À½¼º ºñ¼­, ÀÚµ¿ °í°´ Áö¿ø ½Ã½ºÅÛÀÇ »ç¿ëÀÌ Áõ°¡Çϸ鼭 ÅØ½ºÆ® ÁÖ¼® Åø¿¡ ´ëÇÑ ¼ö¿ä°¡ Áõ°¡Çϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ÅøÀº Àΰ£ÀÇ ¸»À» Á¤È®ÇÏ°Ô ÀÌÇØÇÏ°í ´ëÀÀÇÒ ¼ö ÀÖµµ·Ï AI ¸ðµ¨À» ÇнÀÇÏ´Â µ¥ µµ¿òÀÌ µË´Ï´Ù. À̹ÌÁö ÁÖ¼® Åø ºÐ¾ß°¡ ´õ ³ôÀº ¼ºÀå·üÀ» º¸À̰í ÀÖÁö¸¸, ÅØ½ºÆ® ÁÖ¼® Åø ºÐ¾ß´Â E-Commerce, ÇコÄɾî, ±ÝÀ¶ µîÀÇ »ê¾÷¿¡¼­ NLP ¿ëµµÀÌ ³Î¸® »ç¿ëµÇ°í ÀÖÀ¸¹Ç·Î ´õ ³ôÀº ¸ÅÃâÀ» âÃâÇϰí ÀÖ½À´Ï´Ù. À½¼º ÁÖ¼® ÅøÀº À½¼º ÀνÄ, À½¼º ºñ¼­, À½¼º Àü»ç ¼­ºñ½º µî ºñ±³Àû Àü¹®ÀûÀÎ ºÐ¾ß¿¡¼­ »ç¿ëµÇ±â ¶§¹®¿¡ ÅØ½ºÆ® ÁÖ¼®À̳ª À̹ÌÁö ÁÖ¼®¿¡ ºñÇØ ½ÃÀå Á¡À¯À²ÀÌ ³·½À´Ï´Ù. ÀüüÀûÀ¸·Î µ¥ÀÌÅÍ ÁÖ¼® Åø ½ÃÀåÀº ÅØ½ºÆ®, À̹ÌÁö, À½¼º ÁÖ¼® ºÎ¹®º°·Î ¼ºÀå·ü°ú ¸ÅÃ⠱⿩µµ°¡ ´Ù¸¥ °ÍÀ» ¾Ë ¼ö ÀÖ½À´Ï´Ù.

¼öµ¿ ÁÖ¼® Åø ºÎ¹®, ÁÖ¼® À¯Çüº°·Î ½ÃÀå ÁÖµµ±Ç Àå¾Ç

µ¥ÀÌÅÍ ÁÖ¼® Åø ½ÃÀåÀº ¼öµ¿ ÁÖ¼®, ¹ÝÀÚµ¿ ÁÖ¼®, ÀÚµ¿ ÁÖ¼® µî ÁÖ¼® À¯Çü¿¡ µû¶ó ¼¼ºÐÈ­ÇÒ ¼ö ÀÖ½À´Ï´Ù. ÀÌ Áß °¡Àå ³ôÀº CAGR(2023-2031³â)À» ±â·ÏÇÒ °ÍÀ¸·Î ¿¹»óµÇ´Â ºÎ¹®Àº ÀÚµ¿ ¾î³ëÅ×ÀÌ¼Ç Åø ºÎ¹®ÀÔ´Ï´Ù. ÀÚµ¿ ¾î³ëÅ×À̼ÇÀº AI ¹× ML ¾Ë°í¸®ÁòÀ» Ȱ¿ëÇÏ¿© ¹Ì¸® Á¤ÀÇµÈ ÆÐÅÏÀ̳ª ¸ðµ¨À» ±â¹ÝÀ¸·Î µ¥ÀÌÅÍ¿¡ ÀÚµ¿À¸·Î ¶óº§À» ºÙÀÌ´Â °ÍÀ» ¸»ÇÕ´Ï´Ù. ÄÄÇ»ÅÍ ºñÀü°ú ÀÚ¿¬¾î ó¸® ±â¼úÀÇ ¹ßÀüÀ¸·Î ÀÚµ¿ ¾î³ëÅ×À̼ÇÀÇ Á¤È®µµ¿Í È¿À²¼ºÀÌ Å©°Ô Çâ»óµÇ¾î ±× äÅÃÀÌ È®´ëµÇ°í ÀÖ½À´Ï´Ù. ±â¾÷µéÀº ´ë·®ÀÇ µ¥ÀÌÅÍ¿¡ ÁÖ¼®À» ´Þ±â À§ÇÑ ÀÚµ¿È­µÈ ¼Ö·ç¼ÇÀ» ã°í ÀÖÀ¸¸ç, À̸¦ ÅëÇØ ½Ã°£À» Àý¾àÇϰí ÀηÂÀ» Àý°¨ÇÒ ¼ö ÀÖ½À´Ï´Ù. ÇÑÆí, ¸ÅÃâ Ãø¸é¿¡¼­´Â 2022³â ¼öµ¿ ÁÖ¼® Åø ºÎ¹®ÀÌ °¡Àå ³ôÀº Á¡À¯À²À» Â÷ÁöÇÒ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù. ¼öµ¿ ¾î³ëÅ×À̼ÇÀº ƯÁ¤ °¡À̵å¶óÀΰú ¿ä±¸»çÇ׿¡ µû¶ó »ç¶÷ÀÌ Á÷Á¢ µ¥ÀÌÅÍ¿¡ ¶óº§À» ºÙÀÌ´Â ¹æ½ÄÀ¸·Î ÀÌ·ç¾îÁý´Ï´Ù. ÀÌ ÁÖ¼® À¯ÇüÀº ³ôÀº Á¤È®µµ¿Í ǰÁúÀ» º¸ÀåÇÏÁö¸¸, ƯÈ÷ ´ë±Ô¸ð µ¥ÀÌÅÍ ¼¼Æ®ÀÇ °æ¿ì ½Ã°£°ú ºñ¿ëÀÌ ¸¹ÀÌ ¼Ò¿äµÉ ¼ö ÀÖ½À´Ï´Ù. ±×·¯³ª ½Å·Ú¼º°ú º¹ÀâÇÑ ÁÖ¼® ÀÛ¾÷À» ó¸®ÇÒ ¼ö ÀÖ´Â ´É·ÂÀ¸·Î ÀÎÇØ ¼öµ¿ ÁÖ¼®Àº ÀÇ·á, ±ÝÀ¶, ¹ý·ü µîÀÇ »ê¾÷¿¡¼­ ³Î¸® »ç¿ëµÇ°í ÀÖ½À´Ï´Ù. ¹ÝÀÚµ¿ ¾î³ëÅ×ÀÌ¼Ç ÅøÀº ¼öµ¿ ¾î³ëÅ×À̼ÇÀ̳ª ÀÚµ¿ ¾î³ëÅ×À̼ǿ¡ ºñÇØ ½ÃÀå Á¡À¯À²Àº ÀÛÁö¸¸ Áß¿äÇÑ À§Ä¡¸¦ Â÷ÁöÇϰí ÀÖ½À´Ï´Ù. ¹ÝÀÚµ¿ ¾î³ëÅ×À̼ÇÀº Àΰ£ÀÇ Àü¹® Áö½Ä°ú ÀÚµ¿È­µÈ ¾Ë°í¸®ÁòÀ» °áÇÕÇÑ °ÍÀ¸·Î, ¾î³ëÅ×ÀÌ¼Ç ´ã´çÀÚ°¡ Ãʱ⠾î³ëÅ×À̼ÇÀ» Á¦°øÇÏ¿© AI ¸ðµ¨À» À¯µµÇϰí, ¸ðµ¨Àº ÈÄ¼Ó µ¥ÀÌÅÍ¿¡ ´ëÇÑ ¾î³ëÅ×À̼ÇÀ» Á¡ÁøÀûÀ¸·Î ÇнÀÇÕ´Ï´Ù. ÀÌ Á¢±Ù ¹æ½ÄÀº Á¤È®µµ¿Í È¿À²¼ºÀÇ ±ÕÇüÀ» ¸ÂÃâ ¼ö ÀÖ½À´Ï´Ù. ƯÈ÷ Á¦ÇÑµÈ ·¹À̺íÀÌ ºÙÀº µ¥ÀÌÅ͸¦ ´Ù·ç°Å³ª Àü¹® Áö½ÄÀÌ ÇÊ¿äÇÑ °æ¿ì¿¡ ƯÈ÷ È¿°úÀûÀÔ´Ï´Ù. ¿ä¾àÇϸé, µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀåÀº ¼öµ¿, ¹ÝÀÚµ¿, ÀÚµ¿ ¾î³ëÅ×ÀÌ¼Ç ºÎ¹®º°·Î ¼ºÀå·ü°ú ¸ÅÃ⠱⿩µµ°¡ ´Ù¸£¸ç, ÀÚµ¿ ¾î³ëÅ×À̼ÇÀÌ °¡Àå ³ôÀº CAGRÀ» º¸ÀÌ°í ¼öµ¿ ¾î³ëÅ×À̼ÇÀÌ °¡Àå ³ôÀº ¸ÅÃâÀ» âÃâÇϰí ÀÖ½À´Ï´Ù.

ºÏ¹Ì, ¸ÅÃâ 1À§, ¾Æ½Ã¾ÆÅÂÆò¾çÀÌ ¼ºÀå ÁÖµµ

Áö¿ªº°·Î´Â ºÏ¹Ì°¡ ¾÷°è Àü¹ÝÀÇ ³ôÀº AI ¹× ML ±â¼ú äÅ÷ü·Î ÀÎÇØ Å« ÆøÀÇ ¼ºÀåÀ» º¸ÀÏ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù. ÁÖ¿ä ±â¼ú ±â¾÷, ¿¬±¸±â°ü, AI ½ºÅ¸Æ®¾÷ÀÇ Á¸Àç°¡ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø¿¡ ´ëÇÑ ¼ö¿ä¸¦ °ßÀÎÇϰí ÀÖ½À´Ï´Ù. À¯·´Àº µ¥ÀÌÅÍ ÇÁ¶óÀ̹ö½Ã Áß½Ã, GDPR°ú °°Àº ÄÄÇöóÀ̾𽺠±ÔÁ¦ °­È­, Á¤È®Çϰí À±¸®ÀûÀÎ µ¥ÀÌÅÍ ¾î³ëÅ×À̼ÇÀÇ Çʿ伺ÀÌ ´ëµÎµÇ¸é¼­ Å« ¼ºÀå ÀáÀç·ÂÀ» º¸À̰í ÀÖ½À´Ï´Ù. ¾Æ½Ã¾ÆÅÂÆò¾çÀº Áß±¹, Àεµ, Çѱ¹ µî ±¹°¡µéÀÇ ±Þ¼ÓÇÑ µðÁöÅÐ Àüȯ°ú AI ÀÎÇÁ¶ó¿¡ ´ëÇÑ ÅõÀÚ Áõ°¡·Î ÀÎÇØ °­·ÂÇÑ ¼ºÀå¼¼¸¦ º¸ÀÏ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù. ÀÌ Áö¿ª¿¡¼­´Â ±â¼ú¿¡ Á¤ÅëÇÑ Àα¸ Áõ°¡¿Í AI ±â¹Ý »ê¾÷ÀÇ ºÎ»óÀ¸·Î µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç ÅøÀÇ Ã¤ÅÃÀÌ Áõ°¡Çϰí ÀÖÀ¸¸ç, ¾Æ½Ã¾ÆÅÂÆò¾çÀº ½ÅÈï±¹À¸·Î¼­ AI ±â¼ú¿¡ ´ëÇÑ °ü½ÉÀÌ ³ô¾ÆÁö¸é¼­ °¡Àå ³ôÀº CAGRÀ» º¸ÀÌ´Â Áö¿ªÀ¸·Î Å« ÀáÀç·ÂÀ» °¡Áö°í ÀÖ½À´Ï´Ù. Áß±¹ÀÇ 'Â÷¼¼´ë ÀΰøÁö´É °³¹ß °èȹ'°ú °°Àº ³ë·ÂÀ¸·Î ÀÎÇØ ÀÌ Áö¿ªÀº AI°¡ Å©°Ô ¼ºÀåÇÒ °ÍÀ¸·Î ¿¹»óµÇ¸ç, ÀÌ·Î ÀÎÇØ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø¿¡ ´ëÇÑ ¼ö¿ä¸¦ ÃËÁøÇÒ °ÍÀ¸·Î º¸ÀÔ´Ï´Ù. ÇÑÆí, ºÏ¹Ì´Â ÷´ÜÀÎ ±â¼ú ȯ°æ, AIÀÇ Á¶±â µµÀÔ, ½ÃÀåÀ» ¼±µµÇÏ´Â À¯¸í ±â¾÷ÀÇ Á¸Àç·Î ÀÎÇØ ÇöÀç ¸ÅÃâ ºñÁß¿¡¼­ ¼±µÎ¸¦ ´Þ¸®°í ÀÖ½À´Ï´Ù. ÀÌ Áö¿ªÀº R&D Ȱµ¿¿¡ ´ëÇÑ ÅõÀÚ°¡ Ȱ¹ßÇϰí AI ¿ëµµ ½ÃÀåÀÌ ¼º¼÷ÇØ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå¿¡¼­ ¸ÅÃâ ¿ìÀ§¸¦ Á¡ÇÏ´Â µ¥ ±â¿©Çϰí ÀÖ½À´Ï´Ù. Àü¹ÝÀûÀ¸·Î ºÏ¹Ì°¡ ¸ÅÃâ Ãø¸é¿¡¼­ ¿ìÀ§¸¦ Á¡Çϰí ÀÖ´Â °¡¿îµ¥, ¾Æ½Ã¾ÆÅÂÆò¾çÀº À¯¸®ÇÑ Á¤ºÎ Á¤Ã¥°ú AI ±â¼úÀÇ ±Þ¼ÓÇÑ µµÀÔÀ¸·Î ÀÎÇØ °¡Àå ³ôÀº CAGR·Î ³ôÀº ¼ºÀå ÀáÀç·ÂÀ» º¸¿©ÁÖ°í ÀÖ½À´Ï´Ù.

¿¹Ãø ±â°£ Áß ½ÃÀå °æÀïÀº ´õ¿í °ÝÈ­

µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀåÀº °æÀïÀÌ Ä¡¿­Çϸç, ¿©·¯ ÁÖ¿ä ¾÷üµéÀÌ ½ÃÀå Á¡À¯À²À» ³õ°í °æÀïÇϰí ÀÖ½À´Ï´Ù. ÀÌ ¾÷üµéÀº ´Ù¾çÇÑ µ¥ÀÌÅÍ ÁÖ¼® Åø¿Í ¼­ºñ½º¸¦ Á¦°øÇÏ¿© ´Ù¾çÇÑ »ê¾÷ ºÐ¾ßÀÇ Á¶Á÷ÀÇ ´Ù¾çÇÑ ¿ä±¸¿¡ ºÎÀÀÇϰí ÀÖ½À´Ï´Ù. ½ÃÀå ¼±µµ ±â¾÷À¸·Î´Â Alegion, Appen Limited, Cogito Tech LLC, Figure Eight Inc. ½ÃÀåÀÇ ÁÖ¿ä °æÀï Æ®·»µå Áß Çϳª´Â ¾î³ëÅ×ÀÌ¼Ç ÇÁ·Î¼¼½ºÀÇ Á¤È®¼º°ú È¿À²¼º Çâ»ó¿¡ ÃÊÁ¡À» ¸ÂÃß°í ÀÖ´Ù´Â Á¡ÀÔ´Ï´Ù. ±â¾÷µéÀº °í±Þ AI ¹× ML ±â¼ú¿¡ ÅõÀÚÇÏ¿© ¼öµ¿ ÁÖ¼®¿¡ ´ëÇÑ ÀÇÁ¸µµ¸¦ ³·Ãß°í ½Ã°£°ú ÀÚ¿øÀ» Àý¾àÇÒ ¼ö ÀÖ´Â ÀÚµ¿ ÁÖ¼® ÅøÀ» °³¹ßÇϰí ÀÖ½À´Ï´Ù. ÄÄÇ»ÅÍ ºñÀü, ÀÚ¿¬¾î ó¸®, µö·¯´×°ú °°Àº ±â¼úÀ» Ȱ¿ëÇÏ¿© ÁÖ¼®ÀÇ Á¤È®µµ¿Í ¼Óµµ¸¦ °³¼±Çϰí ÁÖ¼®ÀÌ ´Þ¸° µ¥ÀÌÅÍ ¼¼Æ®ÀÇ Àü¹ÝÀûÀΠǰÁúÀ» Çâ»ó½Ã۰í ÀÖ½À´Ï´Ù. ¶Ç ´Ù¸¥ °æÀï Æ®·»µå´Â µ¥ÀÌÅÍ ÇÁ¶óÀ̹ö½Ã ¹× º¸¾È¿¡ ´ëÇÑ °ü½ÉÀÔ´Ï´Ù. µ¥ÀÌÅÍ À¯Ãâ°ú ÇÁ¶óÀ̹ö½Ã ±ÔÁ¦¿¡ ´ëÇÑ ¿ì·Á°¡ Ä¿Áü¿¡ µû¶ó µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ÇÁ·Î¹ÙÀÌ´õµéÀº ±â¹Ð µ¥ÀÌÅ͸¦ º¸È£Çϱâ À§ÇØ °­·ÂÇÑ º¸¾È Á¶Ä¡¸¦ ½ÃÇàÇϰí ÀÖ½À´Ï´Ù. µ¥ÀÌÅÍ ÇÁ¶óÀ̹ö½Ã¸¦ º¸ÀåÇÏ°í ±ÔÁ¦ ¿ä°ÇÀ» ÃæÁ·Çϱâ À§ÇØ ¾Ïȣȭ ±â¼ú, ¾×¼¼½º Á¦¾î ¹× ÄÄÇöóÀ̾𽺠ÇÁ·¹ÀÓ¿öÅ©¸¦ äÅÃÇϰí ÀÖ½À´Ï´Ù. µ¥ÀÌÅÍ º¸¾ÈÀ» ¿ì¼±½ÃÇÔÀ¸·Î½á °í°´°úÀÇ ½Å·Ú °ü°è¸¦ ±¸ÃàÇÏ°í ½ÃÀå¿¡¼­ Â÷º°È­¸¦ ²ÒÇÏ´Â °ÍÀÌ ¸ñÇ¥´Ù. ¶ÇÇÑ Çù¾÷°ú ÆÄÆ®³Ê½ÊÀº µ¥ÀÌÅÍ ÁÖ¼® Åø ½ÃÀå ±â¾÷µéÀÌ Ã¤ÅÃÇÏ´Â ÁÖ¿ä Àü·«ÀÔ´Ï´Ù. ¸¹Àº ±â¾÷µéÀÌ AI Ç÷§Æû ÇÁ·Î¹ÙÀÌ´õ, µ¥ÀÌÅÍ ÇÁ·Î¹ÙÀÌ´õ, »ê¾÷º° Àü¹®°¡¿Í Àü·«Àû Á¦ÈÞ¸¦ ¸Î¾î ÅëÇÕ ¼Ö·ç¼ÇÀ» Á¦°øÇÕ´Ï´Ù. ÀÌ·¯ÇÑ Á¦ÈÞ¸¦ ÅëÇØ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç ÅøÀ» ±âÁ¸ AI ¿öÅ©Ç÷ο쿡 ¿øÈ°ÇÏ°Ô ÅëÇÕÇÏ°í ¾î³ëÅ×ÀÌ¼Ç ÇÁ·Î¼¼½ºÀÇ ±â´ÉÀ» °­È­ÇÒ ¼ö ÀÖ½À´Ï´Ù. ÆÄÆ®³Ê½ÊÀ» ÅëÇØ ±â¾÷Àº °í°´ÀÇ ´Ù¾çÇÑ ¿ä±¸¿¡ ºÎÀÀÇÏ°í ¿£µåÅõ¿£µå ¼Ö·ç¼ÇÀ» Á¦°øÇÒ ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ ½ÃÀå¿¡¼­ °æÀï·ÂÀ» À¯ÁöÇϱâ À§Çؼ­´Â Áö¼ÓÀûÀÎ Çõ½Å°ú Á¦Ç° °³¹ßÀÌ Áß¿äÇÕ´Ï´Ù. µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ÇÁ·Î¹ÙÀÌ´õµéÀº ¾÷°èÀÇ »õ·Î¿î ¿ä±¸»çÇ×°ú ±â¼ú ¹ßÀü¿¡ ´ëÀÀÇϱâ À§ÇØ Áö¼ÓÀûÀ¸·Î Á¦Ç°À» ¹ßÀü½Ã۰í ÀÖ½À´Ï´Ù. »õ·Î¿î ÁÖ¼® ±â¼úÀ» µµÀÔÇϰí, ´Ù¾çÇÑ µ¥ÀÌÅÍ À¯Çü(ÅØ½ºÆ®, À̹ÌÁö, À½¼º, µ¿¿µ»ó µî)¿¡ ´ëÇÑ Áö¿øÀ» È®´ëÇϰí, »ç¿ëÀÚ ÀÎÅÍÆäÀ̽º¿Í Á¶ÀÛ¼ºÀ» °³¼±ÇÏ´Â µî ´Ù¾çÇÑ ³ë·ÂÀ» ±â¿ïÀ̰í ÀÖ½À´Ï´Ù. ÀÌµé ±â¾÷Àº ±â¼ú ¹ßÀüÀÇ ÃÖÀü¼±¿¡ ¼­¼­ ½ÃÀåÀÇ ÁøÈ­ÇÏ´Â ¿ä±¸¿¡ ºÎÀÀÇÏ´Â ÃÖ÷´Ü ¼Ö·ç¼ÇÀ» Á¦°øÇϱâ À§ÇØ ³ë·ÂÇϰí ÀÖ½À´Ï´Ù.

¸ñÂ÷

Á¦1Àå ¼­¹®

  • ¸®Æ÷Æ® ³»¿ë
    • º¸°í¼­ÀÇ ¸ñÀû
    • ´ë»ó µ¶ÀÚ
    • ÁÖ¿ä Á¦°ø »óǰ
  • ½ÃÀå ¼¼ºÐÈ­
  • Á¶»ç ¹æ¹ý

Á¦2Àå ÁÖ¿ä ¿ä¾à

Á¦3Àå µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : °æÀï ºÐ¼®

  • ÁÖ¿ä º¥´õÀÇ ½ÃÀå Æ÷Áö¼Å´×
  • º¥´õ°¡ äÅÃÇÏ´Â Àü·«
  • ÁÖ¿ä »ê¾÷ Àü·«
  • Tier ºÐ¼® : 2022 vs 2031

Á¦4Àå µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : °Å½ÃÀû ºÐ¼®°ú ½ÃÀå ¿ªÇÐ

  • ¼­·Ð
  • ¼¼°èÀÇ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå ±Ý¾× 2021-2031
  • ½ÃÀå ¿ªÇÐ
    • ½ÃÀå ÃËÁø¿äÀÎ
    • ½ÃÀå ¾ïÁ¦¿äÀÎ
    • ÁÖ¿ä °úÁ¦
    • ÁÖ¿ä ±âȸ
  • ÃËÁø¿äÀΰú ¾ïÁ¦¿äÀÎÀÇ ¿µÇ⠺м®
  • ½Ã¼Ò ºÐ¼®

Á¦5Àå µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : À¯Çüº° 2021-2031

  • ½ÃÀå °³¿ä
  • ¼ºÀ塤¸ÅÃ⠺м® : 2022 vs 2031
  • ½ÃÀå ¼¼ºÐÈ­
    • ÅØ½ºÆ®
    • À̹ÌÁö/ºñµð¿À
    • À½¼º

Á¦6Àå µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾î³ëÅ×ÀÌ¼Ç À¯Çüº° 2021-2031

  • ½ÃÀå °³¿ä
  • ¼ºÀ塤¸ÅÃ⠺м® : 2022 vs 2031
  • ½ÃÀå ¼¼ºÐÈ­
    • ¼öµ¿
    • ¹ÝÀÚµ¿
    • ÀÚµ¿

Á¦7Àå µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾÷Á¾º° 2021-2031

  • ½ÃÀå °³¿ä
  • ¼ºÀ塤¸ÅÃ⠺м® : 2022 vs 2031
  • ½ÃÀå ¼¼ºÐÈ­
    • IT
    • ÀÚµ¿Â÷
    • Á¤ºÎ
    • ÇコÄɾî
    • ±ÝÀ¶ ¼­ºñ½º
    • ¼Ò¸Å
    • ±âŸ

Á¦8Àå µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¼­ºñ½ºº° 2021-2031

  • ½ÃÀå °³¿ä
  • ¼ºÀ塤¸ÅÃ⠺м® : 2022 vs 2031
  • ½ÃÀå ¼¼ºÐÈ­
    • Àü¹® ¼­ºñ½º
    • ¸Å´ÏÁöµå ¼­ºñ½º

Á¦9Àå ºÏ¹ÌÀÇ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå 2021-2031

  • ½ÃÀå °³¿ä
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾î³ëÅ×ÀÌ¼Ç À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾÷Á¾º° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¼­ºñ½ºº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : Áö¿ªº° 2021-2031
    • ºÏ¹Ì
      • ¹Ì±¹
      • ij³ª´Ù
      • ±âŸ ºÏ¹Ì Áö¿ª

Á¦10Àå ¿µ±¹¡¤À¯·´¿¬ÇÕÀÇ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå 2021-2031

  • ½ÃÀå °³¿ä
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾î³ëÅ×ÀÌ¼Ç À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾÷Á¾º° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¼­ºñ½ºº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : Áö¿ªº° 2021-2031
    • ¿µ±¹¡¤À¯·´¿¬ÇÕ
      • ¿µ±¹
      • µ¶ÀÏ
      • ½ºÆäÀÎ
      • ÀÌÅ»¸®¾Æ
      • ÇÁ¶û½º
      • ±âŸ À¯·´ Áö¿ª

Á¦11Àå ¾Æ½Ã¾ÆÅÂÆò¾çÀÇ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå 2021-2031

  • ½ÃÀå °³¿ä
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾î³ëÅ×ÀÌ¼Ç À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾÷Á¾º° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¼­ºñ½ºº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : Áö¿ªº° 2021-2031
    • ¾Æ½Ã¾ÆÅÂÆò¾ç
      • Áß±¹
      • ÀϺ»
      • Àεµ
      • È£ÁÖ
      • Çѱ¹
      • ±âŸ ¾Æ½Ã¾ÆÅÂÆò¾ç

Á¦12Àå ¶óÆ¾¾Æ¸Þ¸®Ä«ÀÇ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå 2021-2031

  • ½ÃÀå °³¿ä
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾î³ëÅ×ÀÌ¼Ç À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾÷Á¾º° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¼­ºñ½ºº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : Áö¿ªº° 2021-2031
    • ¶óƾ¾Æ¸Þ¸®Ä«
      • ºê¶óÁú
      • ¸ß½ÃÄÚ
      • ±âŸ ¶óƾ¾Æ¸Þ¸®Ä« Áö¿ª

Á¦13Àå Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«ÀÇ µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå 2021-2031

  • ½ÃÀå °³¿ä
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾î³ëÅ×ÀÌ¼Ç À¯Çüº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¾÷Á¾º° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : ¼­ºñ½ºº° 2021-2031
  • µ¥ÀÌÅÍ ¾î³ëÅ×ÀÌ¼Ç Åø ½ÃÀå : Áö¿ªº° 2021-2031
    • Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«
      • GCC
      • ¾ÆÇÁ¸®Ä«
      • ±âŸ Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«

Á¦14Àå ±â¾÷ °³¿ä

  • Alegion
  • Appen Limited
  • Cogito Tech LLC
  • Figure Eight Inc.(acquired by Appen Limited)
  • Labelbox Inc.
  • ±âŸ ÁÖ¸ñ ±â¾÷
KSA 23.07.04

The data annotation tools market is expected to grow at a CAGR of 25% during the forecast period of 2023 to 2031. The market has been experiencing significant growth in recent years, driven by the increasing demand for annotated data in various industries such as artificial intelligence (AI), machine learning (ML), and computer vision. Data annotation refers to the process of labeling or tagging data to make it understandable and usable for AI and ML algorithms. These tools play a crucial role in training and improving the accuracy of AI models by providing high-quality annotated datasets. One of the key factors contributing to the growth of the data annotation tools market is the rapid advancement in AI technologies. As AI applications become more prevalent across industries, the need for annotated data to train these models has surged. Data annotation tools offer efficient and scalable solutions for organizations to annotate large volumes of data, saving time and effort compared to manual annotation methods. Moreover, the increasing availability of big data and the growing adoption of cloud computing have further fueled the demand for data annotation tools. With the proliferation of digital content and the rise of internet-connected devices, there is a vast amount of unstructured data that require annotation to extract meaningful insights. Cloud-based data annotation tools provide flexibility, accessibility, and collaborative features, making them highly preferred by organizations of all sizes.

Increasing Adoption of Artificial Intelligence (AI) and Machine Learning (ML) Technologies

The rapid adoption of AI and ML technologies across industries is a key driver for the data annotation tools market. AI and ML algorithms heavily rely on annotated data for training and improving their accuracy. As organizations recognize the value of AI and ML in enhancing operational efficiency and gaining competitive advantage, the demand for data annotation tools has surged. Companies such as Google, Amazon, and Microsoft have heavily invested in AI and ML research and development. They have integrated AI capabilities into their products and services, which require large volumes of annotated data. This has led to an increased demand for data annotation tools to support their AI initiatives.

Growing Need for High-Quality Annotated Datasets

The need for high-quality annotated datasets has become crucial for the success of AI and ML projects. Annotated data provides the necessary context and labels for training algorithms effectively. As organizations strive for accurate and reliable AI models, the demand for data annotation tools that can produce high-quality annotations has risen. The quality of annotations directly impacts the performance and reliability of these models. Organizations are investing in data annotation tools to ensure the accuracy and consistency of their annotated datasets.

Expansion of Industries Requiring Annotated Data

The use of annotated data is not limited to a single industry. Various sectors, such as healthcare, automotive, retail, and finance, are increasingly leveraging AI and ML technologies. These industries require annotated data specific to their domains to train models effectively. This expansion of industries requiring annotated data has contributed to the growth of the data annotation tools market. Numerous case studies and success stories across different industries showcase the application of AI and ML technologies. For instance, in healthcare, annotated medical images are used to train algorithms for diagnosis and treatment planning. In autonomous vehicles, annotated data is essential for object detection and recognition. These examples demonstrate the need for data annotation tools across diverse industries.

Privacy and Ethical Concerns

The data annotation tools market faces significant restraints due to privacy and ethical concerns associated with the use of personal data. Annotating data often involves handling sensitive information, such as personally identifiable information (PII), medical records, or financial data. Organizations must ensure compliance with privacy regulations and ethical guidelines to protect the privacy rights of individuals. Failure to address these concerns can lead to legal consequences, reputational damage, and loss of customer trust. Recent incidents of data breaches and misuse of personal data have raised public awareness and regulatory scrutiny around data privacy. The European Union's General Data Protection Regulation (GDPR) and similar data protection laws worldwide impose strict requirements on the collection, processing, and storage of personal data. Violations of these regulations can result in severe penalties and fines. Moreover, ethical considerations surrounding the use of sensitive data, such as facial recognition or biometric data, have sparked debates and calls for responsible AI practices. Organizations operating in the data annotation tools market need to prioritize privacy and ethical considerations by implementing robust data protection measures, ensuring informed consent, and adopting privacy-by-design principles. Addressing these concerns and demonstrating a commitment to responsible data handling practices is essential for sustained growth and market acceptance of data annotation tools.

Text Annotation Tools Segment to Lead the Revenues

The data annotation tools market can be segmented based on the type of data being annotated, including text, image, and audio. Among these segments, the highest CAGR (2023 to 2031) is expected in the image annotation tools segment. With the increasing adoption of computer vision technologies in various industries, such as autonomous vehicles, retail, healthcare, and surveillance, the demand for image annotation tools has witnessed significant growth. Image annotation involves labeling objects, regions of interest, bounding boxes, and semantic segmentation, among others, to train AI models for image recognition and object detection tasks. The complex nature of image data and the need for precise and detailed annotations contribute to the higher growth rate in this segment. On the other hand, in terms of revenue, the text annotation tools segment held the highest share in 2022. Text annotation is crucial for natural language processing (NLP) applications, sentiment analysis, text classification, and language translation. The increasing use of chatbots, voice assistants, and automated customer support systems has driven the demand for text annotation tools. These tools help in training AI models to understand and respond to human language accurately. Although the image annotation tools segment exhibits a higher growth rate, the text annotation tools segment generates higher revenue due to the widespread use of NLP applications across industries such as e-commerce, healthcare, and finance. The audio annotation tools segment, while significant, holds a smaller market share compared to text and image annotation, as it is relatively specialized and finds applications in areas such as speech recognition, voice assistants, and audio transcription services. Overall, the data annotation tools market showcases varying growth rates and revenue contributions across its text, image, and audio annotation segments.

Manual Annotation Tools Segment Dominates the Market by Annotation Type

The data annotation tools market can be further segmented based on the annotation type, which includes manual annotation, semi-supervised annotation, and automatic annotation. Among these segments, the highest CAGR (2023 to 2031) is expected in the automatic annotation tools segment. Automatic annotation leverages AI and ML algorithms to automatically label data based on predefined patterns or models. The advancements in computer vision and natural language processing techniques have significantly improved the accuracy and efficiency of automatic annotation, leading to its growing adoption. Organizations are increasingly seeking automated solutions to annotate large volumes of data, saving time and reducing human effort. On the other hand, in terms of revenue, the manual annotation tools segment held the highest share in 2022. Manual annotation involves human annotators meticulously labeling data based on specific guidelines or requirements. This annotation type ensures high accuracy and quality but can be time-consuming and costly, especially for large datasets. However, due to its reliability and ability to handle complex annotation tasks, manual annotation remains widely used in industries such as healthcare, finance, and legal. The semi-supervised annotation tools segment, while significant, holds a smaller market share compared to manual and automatic annotation. Semi-supervised annotation combines human expertise with automated algorithms, where annotators guide the AI model by providing initial annotations, and the model progressively learns to annotate subsequent data. This approach strikes a balance between accuracy and efficiency. It is particularly useful when dealing with limited labeled data or when expert knowledge is required. In summary, the data annotation tools market experiences varying growth rates and revenue contributions across its manual, semi-supervised, and automatic annotation segments, with automatic annotation demonstrating the highest CAGR and manual annotation generating the highest revenue.

North America Leads by Revenues, While APAC to Lead the Growth

In terms of geographic trends, North America is expected to witness substantial growth due to the high adoption of AI and ML technologies across industries in the region. The presence of major technology companies, research institutions, and AI startups drives the demand for data annotation tools. Europe also exhibits significant growth potential, fuelled by the increasing emphasis on data privacy and compliance regulations such as GDPR, which necessitate accurate and ethical data annotation practices. The Asia Pacific region is poised to experience robust growth due to the rapid digital transformation and increasing investments in AI infrastructure by countries like China, India, and South Korea. The region's expanding tech-savvy population and the rise of AI-driven industries contribute to the adoption of data annotation tools. In terms of the region with the highest CAGR, Asia Pacific holds strong potential due to its emerging economies and a growing focus on AI technologies. With initiatives like China's "New Generation Artificial Intelligence Development Plan," the region is expected to witness significant growth in AI and consequently drive the demand for data annotation tools. In contrast, North America currently leads in terms of revenue percentage, attributed to its advanced technological landscape, early adoption of AI, and the presence of prominent companies driving the market. The region's strong investment in research and development activities and a mature market for AI applications contribute to its revenue dominance in the data annotation tools market. Overall, while North America dominates in terms of revenue, the Asia Pacific region showcases the highest growth potential with the highest CAGR, driven by favorable government policies and the rapid adoption of AI technologies.

Market Competition to Intensify During the Forecast Period

The data annotation tools market is highly competitive, with several key players vying for market share. These players offer a wide range of data annotation tools and services, catering to the diverse needs of organizations across industries. Some of the top players in the market include Alegion, Appen Limited, Cogito Tech LLC, Figure Eight Inc. (acquired by Appen Limited), and Labelbox Inc. One of the key competitive trends in the market is the focus on improving the accuracy and efficiency of annotation processes. Companies are investing in advanced AI and ML technologies to develop automated annotation tools that reduce the reliance on manual annotation, saving time and resources. They are leveraging techniques like computer vision, natural language processing, and deep learning to enhance the accuracy and speed of annotations, thereby improving the overall quality of annotated datasets. Another competitive trend is the emphasis on data privacy and security. With increasing concerns about data breaches and privacy regulations, data annotation tool providers are implementing robust security measures to protect sensitive data. They are adopting encryption techniques, access controls, and compliance frameworks to ensure data privacy and meet regulatory requirements. By prioritizing data security, these companies aim to build trust with their customers and differentiate themselves in the market. Additionally, collaboration and partnerships are key strategies adopted by players in the data annotation tools market. Many companies are forming strategic alliances with AI platform providers, data providers, and industry-specific experts to offer integrated solutions. These collaborations enable the seamless integration of data annotation tools into existing AI workflows and enhance the capabilities of the annotation process. By leveraging partnerships, companies can provide end-to-end solutions, catering to the diverse needs of their customers. Furthermore, continuous innovation and product development are crucial for staying competitive in the market. Data annotation tool providers are constantly evolving their offerings to address emerging industry requirements and technological advancements. They are incorporating new annotation techniques, expanding support for different data types (such as text, image, audio, and video), and improving the user interface and experience. By staying at the forefront of technological advancements, these companies strive to offer cutting-edge solutions that meet the evolving demands of the market.

Historical & Forecast Period

This study report represents an analysis of each segment from 2021 to 2031 considering 2022 as the base year. Compounded Annual Growth Rate (CAGR) for each of the respective segments estimated for the forecast period of 2023 to 2031.

The current report comprises quantitative market estimations for each micro market for every geographical region and qualitative market analysis such as micro and macro environment analysis, market trends, competitive intelligence, segment analysis, porters five force model, top winning strategies, top investment markets, emerging trends, and technological analysis, case studies, strategic conclusions and recommendations and other key market insights.

Research Methodology

The complete research study was conducted in three phases, namely: secondary research, primary research, and expert panel review. A key data point that enables the estimation of the Data Annotation Tools market are as follows:

  • Research and development budgets of manufacturers and government spending
  • Revenues of key companies in the market segment
  • Number of end users and consumption volume, price, and value.

Geographical revenues generate by countries considered in the report:

Micro and macro environment factors that are currently influencing the Data Annotation Tools market and their expected impact during the forecast period.

Market forecast was performed through proprietary software that analyzes various qualitative and quantitative factors. Growth rate and CAGR were estimated through intensive secondary and primary research. Data triangulation across various data points provides accuracy across various analyzed market segments in the report. Application of both top-down and bottom-up approaches for validation of market estimation assures logical, methodical, and mathematical consistency of the quantitative data.

Market Segmentation

Type

  • Text
  • Image/Video
  • Audio

Annotation Type

  • Manual
  • Semi-supervised
  • Automatic

Vertical

  • IT
  • Automotive
  • Government
  • Healthcare
  • Financial Services
  • Retail
  • Others

Service

  • Professional Services
  • Managed Services

Region Segment (2021-2031; US$ Million)

  • North America
    • U.S.
    • Canada
    • Rest of North America
  • UK and European Union
    • UK
    • Germany
    • Spain
    • Italy
    • France
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia Pacific
  • Latin America
    • Brazil
    • Mexico
    • Rest of Latin America
  • Middle East and Africa
    • GCC
    • Africa
    • Rest of Middle East and Africa

Key questions answered in this report:

  • What are the key micro and macro environmental factors that are impacting the growth of the Data Annotation Tools market?
  • What are the key investment pockets with respect to product segments and geographies currently and during the forecast period?
  • Estimated forecast and market projections up to 2031.
  • Which segment accounts for the fastest CAGR during the forecast period?
  • Which market segment holds a larger market share and why?
  • Are low and middle-income economies investing in the Data Annotation Tools market?
  • Which is the largest regional market for the Data Annotation Tools market?
  • What are the market trends and dynamics in emerging markets such as Asia Pacific, Latin America, and Middle East & Africa?
  • Which are the key trends driving Data Annotation Tools market growth?
  • Who are the key competitors and what are their key strategies to enhance their market presence in the Data Annotation Tools market worldwide?

Table of Contents

1. Preface

  • 1.1. Report Description
    • 1.1.1. Purpose of the Report
    • 1.1.2. Target Audience
    • 1.1.3. Key Offerings
  • 1.2. Market Segmentation
  • 1.3. Research Methodology
    • 1.3.1. Phase I - Secondary Research
    • 1.3.2. Phase II - Primary Research
    • 1.3.3. Phase III - Expert Panel Review
    • 1.3.4. Assumptions
    • 1.3.5. Approach Adopted

2. Executive Summary

  • 2.1. Market Snapshot: Global Data Annotation Tools Market
  • 2.2. Global Data Annotation Tools Market, By Type, 2022 (US$ Million)
  • 2.3. Global Data Annotation Tools Market, By Annotation Type, 2022 (US$ Million)
  • 2.4. Global Data Annotation Tools Market, By Vertical, 2022 (US$ Million)
  • 2.5. Global Data Annotation Tools Market, By Service, 2022 (US$ Million)
  • 2.6. Global Data Annotation Tools Market, By Geography, 2022 (US$ Million)
  • 2.7. Attractive Investment Proposition by Geography, 2022

3. Data Annotation Tools Market: Competitive Analysis

  • 3.1. Market Positioning of Key Data Annotation Tools Market Vendors
  • 3.2. Strategies Adopted by Data Annotation Tools Market Vendors
  • 3.3. Key Industry Strategies
  • 3.4. Tier Analysis 2022 Versus 2031

4. Data Annotation Tools Market: Macro Analysis & Market Dynamics

  • 4.1. Introduction
  • 4.2. Global Data Annotation Tools Market Value, 2021 - 2031, (US$ Million)
  • 4.3. Market Dynamics
    • 4.3.1. Market Drivers
    • 4.3.2. Market Restraints
    • 4.3.3. Key Challenges
    • 4.3.4. Key Opportunities
  • 4.4. Impact Analysis of Drivers and Restraints
  • 4.5. See-Saw Analysis

5. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)

  • 5.1. Market Overview
  • 5.2. Growth & Revenue Analysis: 2022 Versus 2031
  • 5.3. Market Segmentation
    • 5.3.1. Text
    • 5.3.2. Image/Video
    • 5.3.3. Audio

6. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)

  • 6.1. Market Overview
  • 6.2. Growth & Revenue Analysis: 2022 Versus 2031
  • 6.3. Market Segmentation
    • 6.3.1. Manual
    • 6.3.2. Semi-supervised
    • 6.3.3. Automatic

7. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)

  • 7.1. Market Overview
  • 7.2. Growth & Revenue Analysis: 2022 Versus 2031
  • 7.3. Market Segmentation
    • 7.3.1. IT
    • 7.3.2. Automotive
    • 7.3.3. Government
    • 7.3.4. Healthcare
    • 7.3.5. Financial Services
    • 7.3.6. Retail
    • 7.3.7. Others

8. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)

  • 8.1. Market Overview
  • 8.2. Growth & Revenue Analysis: 2022 Versus 2031
  • 8.3. Market Segmentation
    • 8.3.1. Professional Services
    • 8.3.2. Managed Services

9. North America Data Annotation Tools Market, 2021-2031, USD (Million)

  • 9.1. Market Overview
  • 9.2. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
  • 9.3. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
  • 9.4. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
  • 9.5. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
  • 9.6.Data Annotation Tools Market: By Region, 2021-2031, USD (Million)
    • 9.6.1.North America
      • 9.6.1.1. U.S.
        • 9.6.1.1.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 9.6.1.1.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 9.6.1.1.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 9.6.1.1.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 9.6.1.2. Canada
        • 9.6.1.2.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 9.6.1.2.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 9.6.1.2.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 9.6.1.2.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 9.6.1.3. Rest of North America
        • 9.6.1.3.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 9.6.1.3.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 9.6.1.3.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 9.6.1.3.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)

10. UK and European Union Data Annotation Tools Market, 2021-2031, USD (Million)

  • 10.1. Market Overview
  • 10.2. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
  • 10.3. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
  • 10.4. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
  • 10.5. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
  • 10.6.Data Annotation Tools Market: By Region, 2021-2031, USD (Million)
    • 10.6.1.UK and European Union
      • 10.6.1.1. UK
        • 10.6.1.1.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 10.6.1.1.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 10.6.1.1.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 10.6.1.1.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 10.6.1.2. Germany
        • 10.6.1.2.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 10.6.1.2.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 10.6.1.2.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 10.6.1.2.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 10.6.1.3. Spain
        • 10.6.1.3.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 10.6.1.3.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 10.6.1.3.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 10.6.1.3.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 10.6.1.4. Italy
        • 10.6.1.4.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 10.6.1.4.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 10.6.1.4.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 10.6.1.4.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 10.6.1.5. France
        • 10.6.1.5.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 10.6.1.5.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 10.6.1.5.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 10.6.1.5.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 10.6.1.6. Rest of Europe
        • 10.6.1.6.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 10.6.1.6.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 10.6.1.6.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 10.6.1.6.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)

11. Asia Pacific Data Annotation Tools Market, 2021-2031, USD (Million)

  • 11.1. Market Overview
  • 11.2. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
  • 11.3. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
  • 11.4. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
  • 11.5. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
  • 11.6.Data Annotation Tools Market: By Region, 2021-2031, USD (Million)
    • 11.6.1.Asia Pacific
      • 11.6.1.1. China
        • 11.6.1.1.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 11.6.1.1.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 11.6.1.1.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 11.6.1.1.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 11.6.1.2. Japan
        • 11.6.1.2.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 11.6.1.2.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 11.6.1.2.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 11.6.1.2.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 11.6.1.3. India
        • 11.6.1.3.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 11.6.1.3.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 11.6.1.3.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 11.6.1.3.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 11.6.1.4. Australia
        • 11.6.1.4.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 11.6.1.4.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 11.6.1.4.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 11.6.1.4.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 11.6.1.5. South Korea
        • 11.6.1.5.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 11.6.1.5.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 11.6.1.5.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 11.6.1.5.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 11.6.1.6. Rest of Asia Pacific
        • 11.6.1.6.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 11.6.1.6.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 11.6.1.6.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 11.6.1.6.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)

12. Latin America Data Annotation Tools Market, 2021-2031, USD (Million)

  • 12.1. Market Overview
  • 12.2. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
  • 12.3. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
  • 12.4. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
  • 12.5. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
  • 12.6.Data Annotation Tools Market: By Region, 2021-2031, USD (Million)
    • 12.6.1.Latin America
      • 12.6.1.1. Brazil
        • 12.6.1.1.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 12.6.1.1.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 12.6.1.1.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 12.6.1.1.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 12.6.1.2. Mexico
        • 12.6.1.2.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 12.6.1.2.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 12.6.1.2.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 12.6.1.2.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 12.6.1.3. Rest of Latin America
        • 12.6.1.3.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 12.6.1.3.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 12.6.1.3.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 12.6.1.3.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)

13. Middle East and Africa Data Annotation Tools Market, 2021-2031, USD (Million)

  • 13.1. Market Overview
  • 13.2. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
  • 13.3. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
  • 13.4. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
  • 13.5. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
  • 13.6.Data Annotation Tools Market: By Region, 2021-2031, USD (Million)
    • 13.6.1.Middle East and Africa
      • 13.6.1.1. GCC
        • 13.6.1.1.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 13.6.1.1.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 13.6.1.1.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 13.6.1.1.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 13.6.1.2. Africa
        • 13.6.1.2.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 13.6.1.2.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 13.6.1.2.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 13.6.1.2.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)
      • 13.6.1.3. Rest of Middle East and Africa
        • 13.6.1.3.1. Data Annotation Tools Market: By Type, 2021-2031, USD (Million)
        • 13.6.1.3.1. Data Annotation Tools Market: By Annotation Type, 2021-2031, USD (Million)
        • 13.6.1.3.1. Data Annotation Tools Market: By Vertical, 2021-2031, USD (Million)
        • 13.6.1.3.1. Data Annotation Tools Market: By Service, 2021-2031, USD (Million)

14. Company Profile

  • 14.1. Alegion
    • 14.1.1. Company Overview
    • 14.1.2. Financial Performance
    • 14.1.3. Product Portfolio
    • 14.1.4. Strategic Initiatives
  • 14.2. Appen Limited
    • 14.2.1. Company Overview
    • 14.2.2. Financial Performance
    • 14.2.3. Product Portfolio
    • 14.2.4. Strategic Initiatives
  • 14.3. Cogito Tech LLC
    • 14.3.1. Company Overview
    • 14.3.2. Financial Performance
    • 14.3.3. Product Portfolio
    • 14.3.4. Strategic Initiatives
  • 14.4. Figure Eight Inc. (acquired by Appen Limited)
    • 14.4.1. Company Overview
    • 14.4.2. Financial Performance
    • 14.4.3. Product Portfolio
    • 14.4.4. Strategic Initiatives
  • 14.5. Labelbox Inc.
    • 14.5.1. Company Overview
    • 14.5.2. Financial Performance
    • 14.5.3. Product Portfolio
    • 14.5.4. Strategic Initiatives
  • 14.6. Other Notable Players
    • 14.6.1. Company Overview
    • 14.6.2. Financial Performance
    • 14.6.3. Product Portfolio
    • 14.6.4. Strategic Initiatives
ºñ±³¸®½ºÆ®
0 °ÇÀÇ »óǰÀ» ¼±Åà Áß
»óǰ ºñ±³Çϱâ
Àüü»èÁ¦