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Gultekin","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3355b0f8-99db-4491-a7ae-7d4dc6d36aa8/official-baris-headshot-cropped-snowflake-wall-2.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"288","width":"288",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-c96fc8e209","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/baris-gultekin/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Baris Gultekin"}},{"authorImage":{"id":"image-daaaa777c4","alt":"Arun Agarwal","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8049a19c-6f69-400d-9f50-c2ee72ba1d88/arun-agarwal.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"653","width":"720",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-1e6b73ec69","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/arun-agarwal/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Arun Agarwal"}},{"authorImage":{"id":"image-c5ee86d83f","alt":"Headshot-style photo of Siddharth Dwivedi","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--be5d59dc-5c8a-479f-94a2-38cd590a83f8/siddharth-dwivedi.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"200","width":"200",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-cd87ffee67","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/siddharth-dwivedi/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Siddharth Dwivedi"}}],"title":{"lines":["Snowflake bringt intelligentere, kontrollierte KI in Unternehmen"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12","blog_text_1163224754":"aem-GridColumn aem-GridColumn--default--12","quote_item_885518376":"aem-GridColumn aem-GridColumn--default--12","blog_text":"aem-GridColumn aem-GridColumn--default--12","blog_text_1142948434":"aem-GridColumn aem-GridColumn--default--12","image_472436161":"aem-GridColumn aem-GridColumn--default--12","quote_item":"aem-GridColumn aem-GridColumn--default--12","image_66602520":"aem-GridColumn aem-GridColumn--default--12","blog_text_1335903993":"aem-GridColumn aem-GridColumn--default--12","blog_text_1059883567":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-1a3c70de0e","text":"\u003Cp\u003EUnternehmen auf der ganzen Welt setzen auf KI, um schnellere Ergebnisse für ihre Kunden zu erzielen. Doch die Skalierung von KI über isolierte Experimente hinaus bleibt eine Herausforderung. Unternehmen, die KI erfolgreich in der Produktion implementieren, haben eines gemeinsam: Sie basieren auf skalierbaren, interoperablen und sicheren KI-Plattformen, die direkt mit den Daten verbunden sind.\u003C/p\u003E\n\u003Cp\u003ESnowflake hat es sich zur Aufgabe gemacht, Unternehmen dabei zu unterstützen, einen erheblichen Mehrwert aus ihren KI-Investitionen zu ziehen. Neben den neuen Funktionen, die durch die \u003Ca href=\"https://www.snowflake.com/de/blog/fuel-ai-strategy-with-connected-accessible-data/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EVorbereitung Ihrer Datengrundlage\u003C/a\u003E und die \u003Ca href=\"https://www.snowflake.com/de/blog/integrated-developer-environment-ai/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EModernisierung des Entwicklerworkflows\u003C/a\u003E die KI-Vorbereitung beschleunigen, freuen wir uns, Innovationen anzukündigen, die Unternehmen den Einsatz und die Bereitstellung von KI in großem Umfang vereinfachen. Mit diesen Funktionen können Sie: \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EMit Snowflake Intelligence können alle Benutzer:innen komplexe Fragen \u003C/b\u003Ein natürlicher Sprache beantworten\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EIntuitive und flexible Entwicklung\u003C/b\u003E von Data Agents auf der Grundlage der Unternehmenskontextdatenbank, einschließlich all Ihrer strukturierten und unstrukturierten Daten, mit erweitertem logischem Denken und direktem Zugriff auf führende Modellanbieter\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EBeschleunigen Sie multimodale KI-Pipelines\u003C/b\u003E mit einfachem SQL, einschließlich eingebetteter Cost-Governance-Kontrollen für einfaches TCO-Management\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EBeschleunigen Sie Produktions-ML\u003C/b\u003E mit fortschrittlichen MLOps-Funktionen und Bereitstellung von ML-Features mit niedriger Latenz für Online-Prognosen\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch2\u003ESnowflake Intelligence: All Ihr Wissen. Ein zuverlässiger Unternehmensagent. \u003C/h2\u003E\n\u003Cp\u003EEinblicke leben schon zu lange in statischen Dashboards und starren Berichten, deren Erstellung viel Zeit kostet, die sich nur schwer navigieren lassen und die oft mehr Fragen aufwerfen, als sie beantworten. \u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/de/blog/snowflake-intelligence-enterprise-ai/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake Intelligence\u003C/a\u003E\u003C/b\u003E (allgemein verfügbar) führt eine neue Kategorie von KI ein: Enterprise Intelligence Agents, die die Arbeitsweise, Zusammenarbeit und Innovation von Unternehmen transformieren, indem sie eine echte datengestützte Kultur fördern. Alle Mitarbeitenden können unabhängig von ihrer technischen Tiefe komplexe Fragen in natürlicher Sprache stellen, das „Warum“ hinter jedem „Was“ aufdecken und sichere Maßnahmen ergreifen – und das alles innerhalb der sicheren und kontrollierten Umgebung von Snowflake.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-00b3a1d7ff","alt":"tyler","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--46d2476f-931a-4713-8f0e-b203f527bc89/snowflake-intelligence-hometochat--intelligent-governed-ai.gif?quality=85&preferwebp=true","lazyEnabled":true,"height":"1080","width":"1920","title":"Figure 1: Snowflake Intelligence is an enterprise intelligence agent that makes organizational insights accessible to every employee through natural language.",":type":"snowflake-site/components/image"},"blog_text_1335903993":{"id":"blog-text-76d182b37f","text":"\u003Cp\u003ESnowflake Intelligence demokratisiert Daten und Einblicke und versetzt alle in die Lage, selbstbewusst zu denken, zu entscheiden und zu handeln. Benutzer:innen können alle Daten, den geschäftlichen Kontext und die umfangreiche Semantik in einer vertrauenswürdigen Umgebung analysieren, begründen und verifizierte Antworten erhalten. \u003C/p\u003E\n\u003Cp\u003EMit \u003Cb\u003EAgentic Document Analytics\u003C/b\u003E (demnächst in Private Preview) können Kunden Snowflake Intelligence für Antworten auf alle Daten nutzen, einschließlich unstrukturierter Daten. Benutzer sind nicht mehr durch herkömmliche RAGs eingeschränkt, bei denen nur Dutzende von Dokumenten pro Abfrage abgefragt werden, sondern Tausende Dokumente mit einer einzigen Abfrage analysiert werden können. Jetzt können Sie aussagekräftige Fragen zu Ihren Dokumenten stellen. So können Sie von „Wie kann ich mein Passwort zurücksetzen?“ auf „Zeige mir eine Anzahl wöchentlicher Erwähnungen nach Produktbereich in meinen Kundensupport-Tickets für die letzten sechs Monate“ umstellen.\u003C/p\u003E\n\u003Cp\u003ESnowflake Intelligence stellt auch die \u003Cb\u003Esemantische Ebene auf Autopilot\u003C/b\u003E (demnächst in Public Preview) – damit Sie sich an die Weiterentwicklung Ihres Unternehmens anpassen können und schneller zu Einblicken gelangen. Durch das Lernen aus Live-Nutzungsdaten und Abfragehistorie werden automatisch neue verifizierte Abfragen zur Überprüfung angezeigt und das Verständnis Ihrer Metriken, Definitionen und Geschäftslogik kontinuierlich verbessert. Snowflake kann automatisch Kontexte aus Tools wie Tableau, manuell eingegebenem SQL und weiteren BI-Plattformen importieren, die demnächst starten und Ihre semantischen Modelle kontinuierlich verbessern. Das Ergebnis sind hochwertigere Modelle, eine schnellere Time-to-Value und Erkenntnisse, denen Sie ab Tag 1 vertrauen können. \u003C/p\u003E\n\u003Cp\u003ESnowflake Intelligence stellt all Ihr Wissen mit einem zuverlässigen Unternehmensagenten zur Verfügung und beseitigt die Barrieren zwischen Fragen und Einblicken. So können Mitarbeitende gemeinsam Innovationen erforschen, schlussfolgern und entwickeln. \u003C/p\u003E\n\u003Ch2\u003EVertrauenswürdige, kontextorientierte Data Agents erstellen \u003C/h2\u003E\n\u003Cp\u003ESnowflake bietet leistungsstarke Tools zur Entwicklung zuverlässiger, produktionsreifer Datenagenten, mit denen Sie all Ihre strukturierten und unstrukturierten Daten genauer abrufen können. Auf diese Agenten kann über die vorkonfigurierte Natural Language Interface in Snowflake Intelligence ganz einfach zugegriffen oder über die Snowflake \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/de/user-guide/snowflake-cortex/cortex-agents\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex Agents API\u003C/a\u003E\u003C/b\u003E (allgemein verfügbar) in benutzerdefinierte Multiagentenarchitekturen integriert werden.\u003C/p\u003E\n\u003Cp\u003ECortex Agents orchestrieren Informationen über strukturierte und unstrukturierte Datenquellen hinweg, um Einblicke zu liefern. Mithilfe von \u003Ca href=\"https://docs.snowflake.com/de/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex Search\u003C/a\u003E and Snowflake \u003Ca href=\"https://docs.snowflake.com/de/user-guide/snowflake-cortex/cortex-analyst\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex Analyst\u003C/a\u003E brechen sie komplexe Abfragen auf, rufen relevante Daten ab und generieren präzise Antworten. Für die Orchestrierung stehen Kunden sofort einsatzbereite LLMs wie OpenAI GPT und Anthropic Claude zur Auswahl. Das ermöglicht Genauigkeit, Effizienz und Governance in jedem Schritt.\u003C/p\u003E\n\u003Cp\u003ESnowflake erleichtert die Entwicklung von Agenten, die domänenspezifische und kontextorientierte Einblicke liefern. Mit den Snowflake \u003Ca href=\"https://docs.snowflake.com/de/user-guide/snowflake-cortex/cortex-knowledge-extensions/cke-overview\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E\u003Cb\u003ECortex Knowledge Extensions\u003C/b\u003E\u003C/a\u003E\u003Cb\u003E\u003C/b\u003E und der \u003Ca href=\"https://docs.snowflake.com/de/user-guide/views-semantic/sharing-semantic-views\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E\u003Cb\u003EFreigabe semantischer Ansichten\u003C/b\u003E\u003C/a\u003E (beide allgemein verfügbar) können Unternehmen die Interaktionen zwischen den Agenten mit Kontext und Wissen von Drittanbietern und Publishern anreichern und gleichzeitig den Schutz geistigen Eigentums und die ordnungsgemäße Zuordnung für Inhaltseigentümer ermöglichen. Zu den neuesten KI-fähigen Datenanbietern und Publishern, die auf \u003Ca href=\"https://app.snowflake.com/marketplace/data-products?sortBy=date_desc&categorySecondary=%5B%2227%22%5D\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Edem Snowflake Marketplace\u003C/a\u003E verfügbar sind, gehören Alation, Allium, The Associated Press, CARTO, CB Insights, Cotality, Crisp, Crunchbase, Deutsche Börse, Equilar, FactSet, Flipside Crypto, Investopedia, IPinfo, LSEG, MSCI, NASDAQ, Nordot, Truestar und The Washington Post. Darüber hinaus bietet Snowflake eine Reihe von KI-fähigen öffentlichen Datenquellen im Snowflake Marketplace, darunter \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV65X/snowflake-public-data-products-snowflake-public-data-cortex-knowledge-extensions\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EEarnings Calls\u003C/a\u003E, \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSTZ67BY9ORD/snowflake-clinical-trials-research-database\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eklinische Studien\u003C/a\u003E, \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSTZ67BY9OQW/snowflake-pubmed-biomedical-research-corpus\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Emedizinische Forschung\u003C/a\u003E und \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSTZ67BY9OQ4/snowflake-snowflake-documentation\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake-Dokumentation\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003EBei der Entwicklung Ihrer Agenten und Anwendungen in Snowflake möchten wir sicherstellen, dass sie interoperabel und mit Ihren anderen Systemen verbunden sind. MCP hat sich zum Standard für Agententools entwickelt, die Verbindungen herstellen und integrieren können. Mit dem \u003Ca href=\"https://docs.snowflake.com/de/user-guide/snowflake-cortex/cortex-agents-mcp\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E\u003Cb\u003Everwalteten MCP-Server von Snowflake\u003C/b\u003E\u003C/a\u003E\u003Cb\u003E\u003C/b\u003E (allgemein verfügbar) können Sie über das MCP-Protokoll eine sichere Verbindung zu Cortex Agents, Cortex Analyst und Cortex Search herstellen. So können Ihre anderen Systeme und Multiagentenarchitekturen über Snowflake AI hochwertigen Geschäftskontext abrufen, ohne dass Ihre Daten jemals die sicheren Governance-Grenzen von Snowflake verlassen müssen.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ESnowflake Interactive Tables und Warehouses\u003C/b\u003E (demnächst allgemein verfügbar) bieten den für agentengestützte Erlebnisse erforderlichen Serving-Layer in Sekundenschnelle und mit hoher Parallelität. \u003C/p\u003E\n\u003Ch2\u003EEntwicklung multimodaler KI-Pipelines mit schnellerer Performance \u003C/h2\u003E\n\u003Cp\u003ESnowflake gestaltet die Datenanalyse mit \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/de/user-guide/snowflake-cortex/aisql?lang=de%2F\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECortex AISQL\u003C/a\u003E\u003C/b\u003E (allgemein verfügbar) neu und ermöglicht die Erstellung von KI-Pipelines, die mit Text, Dokumenten, Bildern, Audio und Video arbeiten – und das alles mit vertrautem SQL, ohne dass separate KI-Dienste erforderlich sind. Von produktionsbereiten Funktionen für multimodale Datenanalysen im großen Maßstab wie AI_COMPLETE, AI_TRANSCRIBE und AI_CLASSIFY bis hin zu dokumentenspezifischen Funktionen wie AI_PARSE_DOC und AI_EXTRACT können Entwickler:innen komplexe Anwendungsfälle in jeder Größenordnung bewältigen. Für ein No-Code-Erlebnis können Benutzer Dateien direkt in den intuitiven \u003Cb\u003EDocument AI Playground\u003C/b\u003E (Public Preview) hochladen,um Informationen zu extrahieren oder zu analysieren und automatisch den entsprechenden Cortex AISQL-Code zu generieren. Dank der Unterstützung von Dynamic Table für Cortex AISQL bleiben KI-gestützte Einblicke durch inkrementelle Updates kontinuierlich auf dem neuesten Stand.\u003C/p\u003E\n\u003Cp\u003ECortex AISQL macht die KI-Verarbeitung für alle SQL-Nutzenden zugänglich und versetzt sie in die Lage, KI-Pipelines auf einfache Weise zu erstellen. So wird der Entwicklungsprozess optimiert, was eine schnellere Bereitstellung und eine bessere Entwicklererfahrung ermöglicht. Darüber hinaus liefert sie leistungsstarke KI-Pipelines. Die Cortex AISQL-Laufzeitergebnisse, die auf Proof-of-Concept-Code mit ähnlich großen Modellen basieren, haben aufgrund der direkt in die Snowflake-Engine integrierten Performance-Optimierungen eine \u003Ca href=\"https://www.snowflake.com/de/blog/ai-sql-query-language/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Edrei- bis siebenmal schnellere Abfragelaufzeit\u003C/a\u003E ergeben als die manueller Pipelines. Dieser Performancegewinn führt zu geringeren Rechenkosten und einer schnelleren Time-to-Value. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_472436161":{"id":"image-45e0d836ae","alt":"2","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--21ab4922-e827-44cb-91f4-19a657d6c10e/figure-2-intelligent-governed-ai.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"660","width":"994","title":"Figure 2: POC: Cortex AISQL improves query speed by three to seven times. Manual AI pipeline runtime results are based on proof-of-concept code using similarly sized models as the AISQL counterpart.",":type":"snowflake-site/components/image"},"blog_text_1142948434":{"id":"blog-text-94fbb31012","text":"\u003Cp\u003EUm die Gesamtbetriebskosten zu vereinfachen, können Benutzer:innen die Vorteile der eingebetteten Cost Governance-Kontrollen nutzen, darunter die Helferfunktion zum Zählen von KI-Token (Public Preview), die rollenbasierte Zugriffskontrolle auf Feature- und Modellebene (Public Preview) und Kostenleitplanken (demnächst allgemein verfügbar). Diese Funktionen erleichtern die Schätzung, Überwachung und Verwaltung von Kosten bei gleichzeitiger Gewährleistung einer unternehmensgerechten Governance. \u003C/p\u003E\n\u003Cp\u003EWeitere Verbesserungen umfassen umfangreiche \u003Ca href=\"https://www.snowflake.com/en/engineering-blog/cortex-aisql-query-optimization/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EOptimierungen der Abfrageperformance\u003C/a\u003E, erweiterte Datentypunterstützung sowie \u003Ca href=\"https://www.snowflake.com/en/engineering-blog/new-sql-features-public-preview/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eneue SQL-Konstrukte und -Funktionen\u003C/a\u003E für eine vereinfachte Datentransformation. Dank der integrierten Beobachtbarkeit der Performance bleiben Pipelines effizient und kosteneffizient in ihrer Größenordnung. \u003C/p\u003E\n\u003Cp\u003EKunden nutzen Cortex AISQL, um leistungsstarke multimodale KI-Pipelines zu entwickeln und so eine schnellere Performance zu geringeren Kosten zu erzielen. Dazu gehören Kunden wie die Allegis Group, ein weltweit führendes Unternehmen für Personal- und Geschäftslösungen, und Eaton, ein Energiemanagementunternehmen, das in mehr als 160 Ländern tätig ist. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-8b18550f5d","alignment":"left","layout":"card","quoteSourceName":" Jack Vorsteg","quoteSourceTitle":"Director of Enterprise Data & Integration bei der Allegis Group","showQuoteIcon":false,"quote":"„Die Document AI-Funktionen von Snowflake waren bei unserer Migration auf unsere neue Contract Lifecycle Management-Plattform von unschätzbarem Wert. Wir haben bereits über 150.000 Dokumente verarbeitet, darunter PDF-, docx- und TIFF-Dateien, und dabei zehntausende manuelle Arbeitsstunden eingespart.“","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"quote_item_885518376":{"id":"quote-item-d92fa22316","alignment":"left","layout":"card","quoteSourceName":"Ross Schlamo","quoteSourceTitle":"Chief Data Officer bei Eaton","showQuoteIcon":false,"quote":"„Für unser Tool zur Sentiment-Analyse in der Support-Organisation hatten wir ursprünglich eine benutzerdefinierte Lösung mit Tools wie Databricks entwickelt, um Anrufprotokolle auszuwerten. Nach dem Wechsel zu Snowflake AISQL konnten wir in nur wenigen Tagen einen funktionierenden Prototyp bereitstellen, die Verarbeitungszeit um 900 % verbessern und im Vergleich zur Eigenentwicklung jährlich 500.000 US-Dollar einsparen. Wir verzeichnen bessere Kundenerlebnisse und planen nun, AISQL für Audiodateien und andere multimodale Daten zu nutzen, um unsere Fähigkeiten bei der Sentiment-Analyse weiter zu optimieren.“","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1163224754":{"id":"blog-text-70ec500be2","text":"\u003Ch2\u003EEntwicklung und Bereitstellung benutzerdefinierter KI/ML-Modelle in der Produktion\u003C/h2\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/features/end-to-end-ml-workflows/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake ML\u003C/a\u003E ist eine vollständig integrierte Sammlung von Entwicklungs-, Inferenz- und Betriebsfunktionen, die direkt auf Ihren kontrollierten Unternehmensdaten basieren. Unsere neuesten Verbesserungen erleichtern es Data Scientists und ML Engineers noch einfacher, produktionsreife Modelle auf einer einzigen Plattform zu entwickeln, bereitzustellen und zu verwalten, ohne Datenabfluss. Kunden wie \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/video/coinbase/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ECoinbase\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/customers/all-customers/case-study/igs-energy/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EIGS Energy\u003C/a\u003E und \u003Ca href=\"https://www.youtube.com/watch?v=fJ2CIFZ_gDY\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EScene+\u003C/a\u003E entwickeln schnellere, skalierbarere ML-Workflows in Snowflake.\u003C/p\u003E\n\u003Cp\u003EFür die Modellentwicklung können ML-Teams nun einfacher hochwertige Modelle erstellen, indem sie einige der leistungsstärksten Modelle über Trainingsläufe hinweg durch nativ integriertes \u003Ca href=\"https://docs.snowflake.com/developer-guide/snowflake-ml/experiments\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E\u003Cb\u003EExperiment-Tracking\u003C/b\u003E\u003C/a\u003E (Public Preview) identifizieren, freigeben und reproduzieren. Das vereinfacht die Zusammenarbeit, verbessert die Reproduzierbarkeit und beschleunigt die Modelliterationen im gesamten Unternehmen. \u003C/p\u003E\n\u003Cp\u003ESnowflake ML bietet \u003Ca href=\"https://www.youtube.com/watch?v=IerdUQNpYIo&embeds_referring_euri=https%3A%2F%2Fdocs.google.com%2F&embeds_referring_origin=https%3A%2F%2Fdocs.google.com&source_ve_path=Mjg2NjY\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Evolle Unterstützung für modulare ML-Workflows\u003C/a\u003E und ermöglicht skalierbare Inferenzen für Modelle, die überall entwickelt wurden, einschließlich externer ML-Plattformen oder Modell-Hubs wie Hugging Face. Dabei bleiben die Governance- und Sicherheitskontrollen von Snowflake erhalten.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_66602520":{"id":"image-987ca56370","alt":"3","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d6eafaf7-a65e-49b8-a089-aec69a76f3b3/figure-3--intelligent-governed-aipng.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"882","width":"1972","title":"Figure 3: Snowflake ML supports scalable inference with models built anywhere. ",":type":"snowflake-site/components/image"},"blog_text_1059883567":{"id":"blog-text-36f094baa3","text":"\u003Cp\u003EZur Inferenz erhalten Benutzer:innen über eine einfache Benutzeroberfläche sofortigen Zugriff auf die besten vortrainierten \u003Cb\u003EHugging Face-Modelle\u003C/b\u003E \u003Cb\u003Emit Ein-Klick-Bereitstellung\u003C/b\u003E (Public Preview) und bieten Funktionen für Prognosen mit niedriger Latenz unter 50 ms mit \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/create-and-serve-online-features-python\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EOnline Feature Store\u003C/a\u003E\u003C/b\u003E (Public Preview). Dies unterstützt kritische Anwendungsfälle wie personalisierte Empfehlungen, Betrugserkennung, Preisoptimierung und Anomalieerkennung, damit Ihre Modelle Entscheidungen auf der Grundlage der neuesten verfügbaren Daten treffen können.\u003C/p\u003E\n\u003Ch2\u003EErfahren Sie mehr und testen Sie jetzt\u003C/h2\u003E\n\u003Cp\u003EKeine KI-Strategie ohne Datenstrategie Mit den neuesten Fortschritten von Snowflake AI machen wir es jedem Unternehmen einfacher und schneller, KI in der Produktion zu entwickeln, bereitzustellen und zu skalieren. Das sorgt für messbare Wirkung und bessere Geschäftsergebnisse. \u003C/p\u003E\n\u003Cp\u003EBeginnen Sie noch heute mit der Entwicklung von KI in Snowflake und folgen Sie diesen Schritt-für-Schritt-Quickstarts: \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/getting-started-with-snowflake-intelligence/index.html?index=..%2F..index#0\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EEnterprise Intelligence Agent erstellen mit Snowflake Intelligence\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/gain_insights_from_unstructured_data/index.html?index=..%2F..index#0\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESkalierbare Verarbeitung unstrukturierter Daten mit Cortex AISQL\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/end-to-end-ml-workflow/index.html?index=..%2F..index#1\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EProduktivsetzung eines End-to-End-Modells in Snowflake ML\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EZukunftsgerichtete Aussagen\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EDieser Artikel enthält zukunftsgerichtete Aussagen, unter anderem über künftige Produktangebote. Diese Aussagen stellen keine Garantie dar, dass diese Angebote wirklich bereitgestellt werden. Die tatsächlichen Ergebnisse und Angebote können abweichen und unterliegen bekannten und unbekannten Risiken und Unsicherheiten. Weitere Informationen finden Sie in unserem jüngsten 10-Q-Formular.\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_1335903993","image_472436161","blog_text_1142948434","quote_item","quote_item_885518376","blog_text_1163224754","image_66602520","blog_text_1059883567"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{"premium_content_bann":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{"premium_content_bann":{"id":"premium-content-banner-48223017bf","contentHeadline":"Generative AI and LLMs for Dummies","contentDescription":"Embrace generative AI and LLMs with the Snowflake Data 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Gultekin","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3355b0f8-99db-4491-a7ae-7d4dc6d36aa8/official-baris-headshot-cropped-snowflake-wall-2.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"288","width":"288",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-c96fc8e209","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/baris-gultekin/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Baris Gultekin"},"authorTitle":"Vice President of AI, Snowflake"},{"authorImage":{"id":"image-daaaa777c4","alt":"Arun Agarwal","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8049a19c-6f69-400d-9f50-c2ee72ba1d88/arun-agarwal.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"653","width":"720",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-1e6b73ec69","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/arun-agarwal/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Arun Agarwal"},"authorTitle":"AI Product Marketing, Snowflake"},{"authorImage":{"id":"image-c5ee86d83f","alt":"Headshot-style photo of Siddharth Dwivedi","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--be5d59dc-5c8a-479f-94a2-38cd590a83f8/siddharth-dwivedi.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"200","width":"200",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-cd87ffee67","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/siddharth-dwivedi/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Siddharth Dwivedi"},"authorTitle":"AI Product Marketing, 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Glückwunsch an den Gewinner der Start-up Challenge 2026: LGND AI"],"type":"heading4",":type":"snowflake-site/components/title-v2"},"button":{"id":"button-3839d99c8e","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/de/blog/startup-challenge-2026-winner/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Herzlichen Glückwunsch an den Gewinner der Start-up Challenge 2026: LGND AI"},"image":{"id":"image-743556bf13","alt":"Startup Challenge logo and Winner text on blue background","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b77db5e9-bdf1-45d7-97b4-582e8d6d66af/blog-startup-challenge-winner-1680x720%25402x.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"1440","width":"3360",":type":"snowflake-site/components/image"},"publicationDate":"JUN 05, 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