{"id":121969,"date":"2025-04-01T13:07:49","date_gmt":"2025-04-01T13:07:49","guid":{"rendered":"https:\/\/barc.com\/?p=121969"},"modified":"2025-04-01T13:18:21","modified_gmt":"2025-04-01T13:18:21","slug":"infografik-observability-ai-innovation","status":"publish","type":"post","link":"https:\/\/barc.com\/de\/infografik-observability-ai-innovation\/","title":{"rendered":"Infografik: Observability for AI Innovation"},"content":{"rendered":"\n<p><strong>Measuring and Optimizing Data Quality, Pipelines and AI\/ML Models<\/strong><\/p>\n\n\n\n<p>Da KI die Risiken und Vorteile von Analytik erh\u00f6ht, werden Transparenz und Verl\u00e4sslichkeit von Input und Output f\u00fcr Unternehmen immer essentieller. Umso wichtiger ist es zu verstehen, warum, wo und wie Anwender Observability implementieren.<\/p>\n\n\n\n<p>Diese BARC-Studie untersucht drei zentrale Disziplinen von Observability: Datenqualit\u00e4t, Datenpipeline und KI\/ML-Modelle \u2013 mit Fokus auf deren Messung, \u00dcberwachung und Optimierung.<\/p>\n\n\n\n<p>In der Infografik finden Sie die wichtigsten Ergebnisse dazu zusammengefasst. Hier gibt es die komplette Studie <a href=\"https:\/\/barc.com\/de\/research\/observability-ai-trends-requirements-practices\/\">\u201eObservability for AI Innovation \u2013 Adoption Trends, Requirements and Best Practices\u201c<\/a> kostenfrei zum Download.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"780\" height=\"9224\" src=\"https:\/\/barc.com\/wp-content\/uploads\/2025\/04\/250313-Observability-for-AI-Infographic.png\" alt=\"Infografik Observability for AI Innovation\" class=\"wp-image-121971\" srcset=\"https:\/\/barc.com\/wp-content\/uploads\/2025\/04\/250313-Observability-for-AI-Infographic.png 780w, https:\/\/barc.com\/wp-content\/uploads\/2025\/04\/250313-Observability-for-AI-Infographic-768x9082.png 768w, https:\/\/barc.com\/wp-content\/uploads\/2025\/04\/250313-Observability-for-AI-Infographic-130x1536.png 130w, https:\/\/barc.com\/wp-content\/uploads\/2025\/04\/250313-Observability-for-AI-Infographic-600x7095.png 600w\" sizes=\"(max-width: 780px) 100vw, 780px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Diese Studie untersucht die Triebkr\u00e4fte f\u00fcr die Einf\u00fchrung von Observability-Prozessen und -Tools, sowie Herausforderungen und bew\u00e4hrte Verfahren f\u00fcr den Erfolg.<\/p>\n","protected":false},"author":109,"featured_media":47323,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center 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