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Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["container_949147658"],":type":"snowflake-site/components/container"},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/de/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":items":{"title_v2":{"id":"title-v2-209c1faa50","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-812c08f902","text":"\u003Cp\u003ESelf-Supervised Learning (SSL) ist ein Ansatz für \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003Emaschinelles Lernen\u003C/a\u003E, der überwachte und unüberwachte Methoden verbindet. Es löst die Herausforderung beim Training von \u003Ca href=\"https://www.snowflake.com/de/fundamentals/ai-models/\"\u003EKI-Modellen\u003C/a\u003E mit riesigen Mengen an gekennzeichneten Daten, deren Erstellung kostspielig und zeitaufwendig ist. Stattdessen trainiert das selbstüberwachte Lernen direkt mit nicht gekennzeichneten Rohdaten, indem es eigene Trainingssignale generiert.\u003C/p\u003E\n\u003Cp\u003ESelbstüberwachtes Lernen reduziert die Abhängigkeit von manueller Kennzeichnung und ermöglicht es KI-Modellen, effizienter zu skalieren und nützliche Darstellungen zu lernen. Selbstüberwachtes Lernen bringt Fortschritte in der Verarbeitung natürlicher Sprache (NLP), Computer Vision und Spracherkennung voran und hilft Unternehmen, ihre \u003Ca href=\"https://www.snowflake.com/de/product/ai/\"\u003EKI-Initiativen\u003C/a\u003E zu beschleunigen und praktische Anwendungen zu erweitern.\u003C/p\u003E\n\u003Cp\u003EIn diesem Artikel untersuchen wir, was selbstüberwachtes Lernen so einzigartig macht und warum es bei der Unterstützung neuer KI-Anwendungen in verschiedenen Branchen immer wichtiger wird.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":items":{"title_v2":{"id":"title-v2-5770010a0c","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist selbstüberwachtes Lernen?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-31135199d5","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ESelbstüberwachtes Lernen ist eine Form des \u003Ca href=\"https://www.snowflake.com/de/product/features/end-to-end-ml-workflows/\"\u003Emaschinellen Lernens\u003C/a\u003E (ML), die es Modellen ermöglicht, aus nicht gekennzeichneten Daten zu lernen. Es kombiniert Elemente sowohl der überwachten als auch der unüberwachten Trainingsmethoden, unterscheidet sich jedoch von beiden:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EÜberwachtes Lernen\u003C/b\u003E basiert auf Datasets, bei denen jedes Beispiel von Menschen gekennzeichnet ist.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EUnüberwachtes Lernen\u003C/b\u003E arbeitet mit Rohdaten, um versteckte Muster oder Cluster zu finden.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESelbstüberwachtes Lernen\u003C/b\u003E generiert direkt aus der Datenstruktur eigene Pseudo-Labels oder Trainingssignale. \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ESelbstüberwachtes Lernen trainiert Modelle, indem es eigene Signale erzeugt, um nützliche Darstellungen zu lernen, ohne dass Menschen umfangreiche manuelle Kennzeichnungen vornehmen müssen. Damit ist sie ein praktischer und skalierbarer Ansatz für den \u003Ca href=\"https://www.snowflake.com/de/product/features/cortex/\"\u003EAufbau von KI-Systemen\u003C/a\u003E, die sich an komplexe \u003Ca href=\"https://www.snowflake.com/de/customers/\"\u003Ereale Aufgaben\u003C/a\u003E anpassen können.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"container_copy_2060034519":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"work",":items":{"title_v2_copy":{"id":"title-v2-4ef9a3d09b","additionalClasses":"headline-decoration","type":"heading2","lines":["Wie funktioniert selbstüberwachtes Lernen?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-32b7bb4c91","text":"\u003Cp\u003EUm effektiv zu sein, muss ein KI-Modell „lernen“, indem es große Datenmengen erfasst, die in seine Reaktionen und Analysen einfließen. Beim herkömmlichen maschinellen Lernen bezieht sich Supervision auf die Verwendung von gekennzeichneten Daten, die von menschlichen Expert:innen erstellt wurden, um die eingegebenen Daten manuell mit der richtigen Ausgabe zu kennzeichnen (z. B. Klassifizierung eines Bildes als „Auto“ oder Kennzeichnung der Stimmung eines Satzes als „positiv“).\u003C/p\u003E\n\u003Cp\u003EDurch die Betreuung dieses Lernens erhält das \u003Ca href=\"https://www.snowflake.com/de/fundamentals/large-language-model/\"\u003EModell\u003C/a\u003E einen Antwortschlüssel, der für das Training hochpräziser Systeme unerlässlich ist. Die manuelle Überwachung ist jedoch zu kostspielig und zeitaufwendig, um eine praktikable Lösung für die riesigen, ständig wachsenden Datasets zu sein, die heute verfügbar sind.\u003C/p\u003E\n\u003Cp\u003ESelbstüberwachtes Lernen löst dieses Problem, indem es Rohdaten ohne Label in eine Quelle der Überwachung verwandelt. Anstatt sich auf kostspielige, gekennzeichnete Datasets zu verlassen, nutzt das selbstüberwachte Lernen die Daten selbst, um Trainingssignale zu generieren. Dieser Prozess hilft ML-Modellen, Muster und Darstellungen zu lernen, die später auf reale Probleme angewendet werden können.\u003C/p\u003E\n\u003Cp\u003EDer Mechanismus hinter dem selbstüberwachten Lernen umfasst zwei Schlüsselphasen: Pretext-Aufgaben und nachgelagerte Aufgaben.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EPretext-Aufgaben\u003C/b\u003E sind künstliche Herausforderungen, die aus den Daten selbst entwickelt wurden. Indem das Modell sie löst, lernt es, sinnvolle Strukturen in den Daten zu erfassen. Hier einige Beispiele:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EBei der Verarbeitung natürlicher Sprache sagt das Modell fehlende Wörter in einem Satz voraus.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EIn der Computer Vision stellt das Modell fest, ob ein Bild gedreht wurde oder fehlende Pixel ausfüllt.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EBei der Spracherkennung erkennt das Modell, ob zwei Audiomuster von demselben Lautsprecher stammen.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EDa diese Tasks keine manuellen Label erfordern, können Modelle mit riesigen Datasets trainieren, deren Anmerkungen ansonsten zu kostspielig oder zeitaufwendig wären.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EDownstream-Aufgaben\u003C/b\u003E sind die realen Anwendungen des maschinellen Lernens wie Textklassifizierung, Bilderkennung oder Sprache-zu-Text. Sobald ein Modell mit Pretext-Aufgaben vortrainiert wurde, werden seine gelernten Darstellungen in nachgelagerte Tasks übertragen, die oft nur minimale Optimierung erfordern.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":items":{"title_v2":{"id":"title-v2-8d0b39b83f","additionalClasses":"headline-decoration","type":"heading2","lines":["Selbstüberwachtes Lernen vs. überwachtes und unüberwachtes Lernen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-1142a6c562","text":"\u003Ch3\u003ESelbstüberwachtes Lernen vs. überwachtes Lernen\u003C/h3\u003E\r\n\u003Cp\u003EÜberwachtes Lernen erfordert große gekennzeichnete Datasets, bei denen jeder Input mit einem richtigen Output verbunden ist. So werden beispielsweise Bildklassifizierungsmodelle mit Datasets trainiert, bei denen jedes Bild ein Label wie „Katze“ oder „Hund“ trägt. Diese Label liefern klare Trainingssignale, sind aber teuer und zeitaufwendig in der Erstellung. ​Trotz der Kosten ist überwachtes Lernen eine bevorzugte und effektive Methode für Aufgaben, die höchste Genauigkeit erfordern, wie z. B. die hochkritische medizinische Diagnostik oder die Erkennung von Finanzbetrug, bei denen die Fehlerkosten extrem hoch sind.\u003C/p\u003E\r\n\u003Cp\u003ESelbstüberwachtes Lernen macht manuelle Label überflüssig. Es erstellt Pseudo-Labels direkt aus Rohdaten durch Pretext-Aufgaben wie die Vorhersage fehlender Wörter oder Bilddrehungen. So können sich Modelle automatisch mit riesigen Mengen nicht gekennzeichneten Daten trainieren, was schneller und viel ressourceneffizienter ist als überwachtes Lernen.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESelbstüberwachtes Lernen vs. unüberwachtes Lernen\u003C/h3\u003E\r\n\u003Cp\u003EUnüberwachtes Lernen beruht auch auf nicht gekennzeichneten Daten, aber das Trainingssignal ist ein anderes. Beim unüberwachten Lernen gruppieren oder reduzieren Modelle in der Regel Daten, z. B. Clustering von Kunden in Segmente oder Komprimierung von Daten in weniger Dimensionen. Diese Methoden finden zwar Muster, erzeugen aber oft keine Darstellungen, die sich gut auf andere Aufgaben übertragen lassen. Ein unüberwachtes Modell könnte beispielsweise erfolgreich eine Dokumentensammlung in fünf Themencluster sortieren. Doch das Clustering von Wissen allein reicht selten aus, um ein separates, genaues System wie eine Echtzeit-Sprachübersetzungsanwendung aufzubauen.\u003C/p\u003E\r\n\u003Cp\u003ESelbstüberwachtes Lernen unterscheidet sich dadurch, dass strukturierte Tasks aus Rohdaten generiert werden, wodurch das Modell dazu gebracht wird, Features zu lernen, die später auf praktische nachgelagerte Tasks angewendet werden können. Ein Modell, das für die Vorhersage maskierter Wörter trainiert wurde, lernt beispielsweise Sprachmuster kennen, die auf die Textklassifizierung oder die Beantwortung von Fragen übertragen werden.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESelbstüberwachtes Lernen vs. semiüberwachtes Lernen\u003C/h3\u003E\r\n\u003Cp\u003ESemi-überwachtes Lernen kombiniert eine kleine Menge gekennzeichneter Daten mit einem größeren Pool nicht gekennzeichneter Daten. Der beschriftete Teil verankert das Modell, während der unbeschriftete Teil zusätzlichen Kontext bietet. Eine KI zur Inhaltsmoderation könnte beispielsweise eine kleine Menge manuell gekennzeichneter und unangemessener Bilder oder Kommentare sowie Millionen nicht gekennzeichneter Beiträge verwenden, um das Modell zu trainieren, ähnliche Inhalte in großem Umfang zu identifizieren.\u003C/p\u003E\r\n\u003Cp\u003ESelbstüberwachtes Lernen ist nicht einmal auf einen kleinen gekennzeichneten Datensatz angewiesen. Es generiert automatisch Labels aus den Daten selbst und ist damit besonders wertvoll in Bereichen, in denen gekennzeichnete Daten begrenzt oder teuer sind, wie z. B. bei der medizinischen Bildgebung oder Spracherkennung.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":items":{"title_v2":{"id":"title-v2-7c025057c6","additionalClasses":"headline-decoration","type":"heading2","lines":["Warum brauchen wir selbstüberwachtes Lernen?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-e76416ee59","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ESelbstüberwachtes Lernen adressiert eine der größten Herausforderungen der KI-Entwicklung: die Abhängigkeit von großen gekennzeichneten Datasets (überwachtes Lernen). Die Hürden, die mit der Abhängigkeit von gekennzeichneten Datasets verbunden sind, umfassen:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EKosten und Zeit\u003C/b\u003E: Das manuelle Kennzeichnen riesiger Datasets ist teuer und langsam.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EKnappheit an gekennzeichneten Daten\u003C/b\u003E: In spezialisierten Bereichen wie Gesetzestexten oder proprietären Unternehmensdaten sind gekennzeichnete Beispiele rar, was ein effektives Modelltraining erschwert.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ESSL überwindet diese Grenzen, indem es die riesige Menge an nicht gekennzeichneten Rohdaten, die bereits vorhanden ist, nutzt, um eigene Aufsichtssignale zu erstellen und nützliche Darstellungen zu lernen, ohne dass ein erheblicher manueller Aufwand erforderlich ist. So können groß angelegte Modelle in wichtigen Bereichen effizienter trainiert werden, darunter:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EVerarbeitung natürlicher Sprache (NLP, Natural Language Processing)\u003C/b\u003E: SSL ermöglicht das Training von Large Language Models auf globalen Textdaten ohne manuelle Anmerkungen.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EComputer Vision und Spracherkennung\u003C/b\u003E: SSL reduziert den menschlichen Aufwand beim Beschriften von Bildern oder beim Transkribieren von Audio und verbessert so die Modellgenauigkeit.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":items":{"title_v2":{"id":"title-v2-72e88b0015","additionalClasses":"headline-decoration","type":"heading2","lines":["Vorteile des selbstüberwachten Lernens"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8f38b6a9c4","text":"\u003Cp\u003ESelbstüberwachtes Lernen bietet verschiedene Vorteile, die es für moderne KI-Systeme geeignet machen. Zu den Vorteilen von SSL gehören:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EWeniger Abhängigkeit von gekennzeichneten Daten\u003C/h3\u003E\n\u003Cp\u003ESelbstüberwachtes Lernen macht manuelle Datenkennzeichnungen überflüssig, indem es eigene Trainingssignale direkt aus Rohdaten generiert. Dank dieser Fähigkeiten können Unternehmen mit einer größeren Auswahl an Datasets trainieren und Daten aus verschiedenen Quellen integrieren, wodurch der Kontext von KI für Analytics erweitert und der Wert gesteigert wird. Diese Methode trägt auch dazu bei, KI-Vorteile in komplexen Bereichen zu nutzen, darunter spezielle medizinische Bildanalysen, in denen gekennzeichnete Daten oft Mangelware sind.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EKosteneffiziente Datennutzung\u003C/h3\u003E\n\u003Cp\u003ESelbstüberwachte Lernmodelle generieren ihre eigenen Trainingssignale direkt aus Eingabedaten und minimieren so den Aufwand für kostspielige menschliche Anmerkungen. Durch die Nutzung der Struktur vorhandener nicht gekennzeichneter Daten steigert selbstüberwachtes Lernen den Wert von Daten-Assets ohne zusätzliche Kennzeichnungskosten. Das macht selbstüberwachtes Lernen besonders wertvoll in datenintensiven Bereichen, in denen es zahlreiche nicht gekennzeichnete Informationen gibt.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVerbesserte Generalisierung und Transferlernen\u003C/h3\u003E\n\u003Cp\u003ESelbstüberwachte Lernmodelle erfassen zugrunde liegende Muster in Daten, die sich gut auf neue Aufgaben übertragen lassen. Mit der Optimierung kann dasselbe Modell für mehrere nachgelagerte Anwendungen angepasst werden.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESkalierbarkeit für große Datasets\u003C/h3\u003E\n\u003Cp\u003EEine manuelle Kennzeichnung ist für die riesigen Datasets von heute nicht praktikabel. 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