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Agarwal"}}],"image":{"id":"image-46af155d38","height":"720","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b06aed21-6867-4295-81c2-f1ca198d475c/build2024-ai-ml-blogheader-680x720.jpg?preferwebp=true&quality=85","alt":"Photo illustration of hands on laptop with background screens and the BUILD 2024 logo at the bottom on a blue bar","lazyEnabled":true,"width":"1680",":type":"snowflake-site/components/image"},"timeToRead":"10","publicationDate":"NOV 12, 2024","tag":{"tagText":"Produkt & Technologie","tagColor":"#3F808A"},"title":{"lines":["Schnellere Entwicklung von KI auf Unternehmensebene dank neuer multimodaler Unterstützung, verbesserter Beobachtbarkeit und mehr"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"youtube":"aem-GridColumn 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href=\"https://www.snowflake.com/build/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EBUILD\u003C/a\u003E stellen wir Ihnen leistungsstarke neue Funktionen vor, mit denen Sie generative KI-Anwendungen schneller auf Unternehmensdaten aufbauen und bereitstellen können, ohne dabei das Vertrauen und die Sicherheit zu gefährden. Diese neuen Tools optimieren Workflows, liefern Einblicke in großem Umfang und bringen KI-Apps schnell in Produktion. Kunden wie \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/skai/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESkai\u003C/a\u003E nutzen diese Funktionen, um ihre GenAI-Lösung in nur zwei Tagen statt in Monaten in Produktion zu bringen. \u003C/p\u003E\n\u003Cp\u003ESo beschleunigen Snowflake \u003Ca href=\"https://www.snowflake.com/de/data-cloud/cortex/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECortex AI\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/en/data-cloud/snowflake-ml/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake ML\u003C/a\u003E die Bereitstellung zuverlässiger KI-Lösungen für die kritischsten GenAI-Anwendungen:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ENLP (Natural Language Processing) für Daten-Pipelines: \u003C/b\u003ELarge Language Models (LLMs) bergen ein transformatives Potenzial, aber sie integrieren oft Batch-Inferenzen in Pipelines, was umständlich sein kann. Mit einer einfachen Abfrage können Entwickler:innen nun Stimmungen ableiten oder Kundenbewertungen über Millionen von Datensätzen hinweg kategorisieren, was die Effizienz steigert und Zeit spart. So konnte \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/ts-imagine/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETS Imagine\u003C/a\u003E beispielsweise mit Snowflake Cortex AI skalierbare generative KI implementieren, wodurch die Kosten um 30 % gesenkt und 4.000 Stunden eingespart wurden, die zuvor für manuelle Aufgaben aufgewendet wurden. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDialogorientierte Apps: Zuverlässige und ansprechende Antworten auf Benutzerfragen zu \u003C/b\u003Eerstellen, ist jetzt einfacher und öffnet die Tür für leistungsstarke Anwendungsfälle wie Self-Service-Analytics und Dokumentensuche über \u003Ca href=\"https://www.snowflake.com/build/emea/agenda?agendaPath=session/1496277\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EChatbots.\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGPU-basierte Modellentwicklung und -bereitstellung: \u003C/b\u003EErstellen Sie leistungsstarke, fortschrittliche ML-Modelle mit Ihren bevorzugten Python-Paketen auf GPUs oder CPUs, die sie zur Inferenz in Containern bereitstellen – alles auf derselben Plattform wie Ihre kontrollierten Daten. Kunden wie \u003Ca href=\"https://www.snowflake.com/en/blog/container-runtime-GPU-training-inference/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EAvios, CHG Healthcare und Keysight Technologies\u003C/a\u003E entwickeln bereits containerbasierte Modelle in Snowflake ML.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch2\u003EPerformance der NLP-Pipeline mit kostengünstiger LLM-Batch-Inferenz optimieren \u003C/h2\u003E\n\u003Cp\u003EDank der sich rasant weiterentwickelnden Technologie sind Modelle in verschiedenen Größen, Kontextfenstern und Funktionen verfügbar, sodass es unerlässlich ist, das richtige für Ihren spezifischen Anwendungsfall auszuwählen. Wenn Ihre Dokumente beispielsweise in mehreren Sprachen vorliegen, ist ein LLM mit starken mehrsprachigen Fähigkeiten entscheidend. Für einfachere NLP-Aufgaben wie die Klassifizierung ist ein fortgeschrittenes Modell jedoch möglicherweise übertrieben und ein kleineres LLM effektiver. \u003Ca href=\"https://docs.snowflake.com/de/user-guide/snowflake-cortex/llm-functions\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECortex LLM-Funktionen\u003C/a\u003E bieten Modelle, die für spezifische Anwendungsfälle optimiert sind, wie z. B. Übersetzung, Zusammenfassung und Klassifizierung. Diese skalierbaren Modelle können Millionen von Datensätzen verarbeiten und ermöglichen so den effizienten Aufbau leistungsstarker NLP-Daten-Pipelines. Die Skalierung der LLM-Datenverarbeitung auf Millionen von Datensätzen kann jedoch Herausforderungen bei der Datenübertragung und Orchestrierung mit sich bringen, die durch die benutzerfreundlichen SQL-Funktionen in Snowflake Cortex problemlos gelöst werden können.\u003C/p\u003E\n\u003Ch3\u003EPerformance- und Kostenoptimierung mit einer größeren Auswahl an Modelloptionen\u003C/h3\u003E\n\u003Cp\u003ECortex AI bietet einfachen Zugriff auf branchenführende Modelle über LLM-Funktionen oder REST-APIs, sodass Sie sich auf die Förderung von GenAI-Innovationen konzentrieren können. Wir bieten eine große Auswahl an Modellen in verschiedenen Größen, Kontextfensterlängen und Sprachunterstützung. Jüngste Neuzugänge sind das \u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-invests-in-voyage-ai-optimizing-multilingual-rag/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Emehrsprachige Einbettungsmodell von Voyage\u003C/a\u003E, die Modelle \u003Ca href=\"https://www.snowflake.com/en/blog/meta-llama-enterprise-apps-snowflake-cortex-ai/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ELlama 3.1\u003C/a\u003E und \u003Ca href=\"https://medium.com/snowflake/metas-llama-3-2-50347680b3f4\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E3.2\u003C/a\u003E von Meta sowie das Modell \u003Ca href=\"https://medium.com/snowflake/applying-jamba-instruct-to-long-context-use-cases-in-snowflake-cortex-ai-fcbbb9fc2df3\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EJamba-Instruct von AI21\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003EMit Cortex Playground (demnächst in Public Preview) können Sie Modelle direkt in Snowsight testen. Mit dieser No-Code-Schnittstelle können Sie schnell mit Modellen experimentieren, sie vergleichen und bewerten, sobald sie verfügbar sind. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-41670703fe","height":"938","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ea48f40d-d0f4-4e37-9158-c62daa775f3b/build2024-ai-ml-cortex-playground.jpg?preferwebp=true&quality=85","alt":"3 screenshots showing Snowflake Cortex Playground, the settings controls, and the View SQL capability","lazyEnabled":true,"width":"1432","title":"Abbildung 1: Cortex Playground ist in Public Preview verfügbar und bietet eine No-Code-Schnittstelle für die LLM-Erstellung und den Vergleich von Einstellungen.",":type":"snowflake-site/components/image"},"blog_text_10630711":{"id":"blog-text-d1957627a2","text":"\u003Cp\u003EMithilfe von Modellanpassungstechniken können Sie Modelle für Ihren spezifischen Anwendungsfall optimieren. Snowflake führt \u003Cb\u003Eserverlose Optimierung ein\u003C/b\u003E (demnächst allgemein verfügbar), mit der Entwickler:innen Modelle optimieren können, um Kosten-Performance-Vorteile zu erhöhen. Mit diesem vollständig verwalteten Dienst müssen Entwickler:innen keine eigene Infrastruktur für Training und Inferenz aufbauen oder verwalten. \u003C/p\u003E\n\u003Ch3\u003EMultimodale Unterstützung in COMPLETE-Funktion erhalten\u003C/h3\u003E\n\u003Cp\u003EVerbessern Sie KI-Apps und -Pipelines mit multimodaler Unterstützung für umfassendere Antworten. Mit den neuen GenAI-Funktionen können Entwickler:innen nun multimodale Daten verarbeiten und dabei die relevantesten Informationen in ihren Anwendungen nutzen. Mit den in Snowflake Cortex AI verfügbaren Llama 3.2-Modellen ermöglichen wir die multimodale \u003Cb\u003ELLM-Inferenz\u003C/b\u003E (demnächst in Private Preview) als Teil der \u003Ca href=\"https://docs.snowflake.com/de/user-guide/snowflake-cortex/llm-functions#available-functions\" target=\"_blank\"\u003E\u003Cb\u003EFunktion Cortex COMPLETE\u003C/b\u003E\u003C/a\u003E für Bildeingaben. Die Unterstützung für Audio-, Video- und Bildeinbettungen wird demnächst folgen. Erweiterte multimodale Unterstützung bereichert die Antworten auf verschiedene Aufgaben wie Zusammenfassung, Klassifizierung und Entitätenextraktion über verschiedene Medientypen hinweg. \u003C/p\u003E\n\u003Ch3\u003EMultimodale Analytik mit vertrauter SQL-Syntax\u003C/h3\u003E\n\u003Cp\u003EDatenbankabfragen sind die zugrunde liegende Kraft, die die Einblicke unternehmensübergreifend ausführt und Nutzenden datengestützte Erfahrungen bietet. Traditionell war SQL auf strukturierte Daten beschränkt, die übersichtlich in Tabellen organisiert waren. Snowflake wird neue multimodale SQL-Funktionen einführen (demnächst in Private Preview), mit denen Datenteams analytische Workflows auf unstrukturierten Daten wie Bildern ausführen können. Mit diesen Funktionen können Teams Aufgaben wie semantische Filter und Joins über unstrukturierte Datasets hinweg ausführen – mit der vertrauten SQL-Syntax. \u003C/p\u003E\n\u003Ch3\u003EZuverlässige Verarbeitung großer Inferenzjobs mit bereitgestellter Durchsatzkapazität\u003C/h3\u003E\n\u003Cp\u003EEine einheitliche Benutzererfahrung ist oft ein wichtiger Faktor, wenn Entwickler:innen über Proof of Concept hinausgehen. Mit \u003Cb\u003EProvisional Throughput\u003C/b\u003E (demnächst in Public Preview auf AWS) können Kunden dedizierten Durchsatz reservieren und so eine einheitliche und vorhersehbare Performance für ihre Workloads gewährleisten. Darüber hinaus haben wir die \u003Ca href=\"https://medium.com/snowflake/announcing-cross-region-inference-on-snowflake-cortex-ai-ab3baed935ce\" target=\"_blank\"\u003Eregionsübergreifende Inferenz eingeführt\u003C/a\u003E, damit Sie auf bevorzugte LLMs zugreifen können, auch wenn diese in Ihrer Primärregion nicht verfügbar sind.\u003C/p\u003E\n\u003Ch2\u003ESchneller qualitativ hochwertige dialogorientierte KI-Apps entwickeln \u003C/h2\u003E\n\u003Cp\u003ESnowflake bietet nun neue Tools zur Vereinfachung der Entwicklung und Bereitstellung von dialogorientierten KI-Anwendungen.\u003C/p\u003E\n\u003Ch3\u003EFortschrittliche Dokumentenvorverarbeitung für RAG\u003C/h3\u003E\n\u003Cp\u003EAnfang dieses Jahres haben wir \u003Ca href=\"https://www.snowflake.com/en/blog/cortex-search-ai-hybrid-search/\" target=\"_blank\"\u003E\u003Cb\u003ECortex Search\u003C/b\u003E\u003C/a\u003E eingeführt, um Kundenunternehmen dabei zu helfen, Erkenntnisse aus unstrukturierten Daten zu gewinnen und umfangreiche Dokumentensammlungen in KI-fähige Ressourcen zu verwandeln, ohne komplexe Programmierung. Mit der vollständig verwalteten Abruflösung können Entwickler:innen skalierbare KI-Apps entwickeln, die Erkenntnisse aus unstrukturierten Daten in der sicheren Umgebung von Snowflake extrahieren. Diese Funktion ist besonders leistungsstark, wenn sie mit layoutorientierten Textextraktions- und Chunking-Funktionen kombiniert wird, die Dokumente für den Abruf optimieren, indem die Vorverarbeitung durch kurze SQL-Funktionen optimiert wird.\u003C/p\u003E\n\u003Cp\u003EJetzt können Sie Dokumente mit zwei neuen SQL-Vorverarbeitungsfunktionen schneller KI-tauglich machen. Wir führen optimierte Lösungen für die Verarbeitung von Dokumenten aus Blob-Speichern (e.g., Amazon S3) in Textdarstellungen zur Verwendung in RAG-Anwendungen (Retrieval-Augmented Generation) ein. SQL-Benutzer:innen können nun komplexe Pipelines zur Dokumentenverarbeitung durch einfache SQL-Funktionen von Cortex AI ersetzen, wie z. B.\u003Cb\u003E \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/parse-document\" target=\"_blank\"\u003EPARSE_DOCUMENT\u003C/a\u003E\u003C/b\u003E (Public Preview) und \u003Cb\u003ESPLIT_TEXT_RECURSIVE_CHARACTER\u003C/b\u003E (Private Preview). Die Parsing-Funktion extrahiert Text und Layout aus Dokumenten. Entwickler:innen müssen die Rohdaten nicht vom ursprünglichen Speicherort verschieben. Die Textaufteilungsfunktion sorgt für die Aufteilung von extrahiertem Text in Segmente, die für die Indexierung und den Abruf optimiert sind. \u003Ca href=\"https://www.snowflake.com/en/blog/streamline-rag-document-preprocessing/\" target=\"_blank\"\u003EWeitere Informationen.\u003C/a\u003E\u003C/p\u003E\n\u003Ch3\u003EVerbesserungen bei der dialogorientierten Analytik in Cortex Analyst\u003C/h3\u003E\n\u003Cp\u003EErweitern Sie mit Cortex Analyst den Umfang präziser Self-Service-Analytics in natürlicher Sprache. \u003Cb\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\" target=\"_blank\"\u003ESnowflake Cortex Analyst\u003C/a\u003E\u003C/b\u003E entwickelt sich als vollständig verwalteter Dienst kontinuierlich weiter und bietet dialogorientierte Self-Service-Analytics, mit denen Benutzer:innen nahtlos mit strukturierten Daten in Snowflake interagieren können. Jüngste Aktualisierungen verbessern die Benutzererfahrung und die analytische Tiefe, einschließlich der \u003Cb\u003EUnterstützung von SQL Joins für Star- und Snowflake-Schemas\u003C/b\u003E (Public Preview) bei gleichbleibend hoher Qualität, was komplexere Datenuntersuchungen und umfassendere Einblicke ermöglicht. Darüber hinaus können Nutzende in \u003Cb\u003EMultiturn-Gesprächen\u003C/b\u003E (Public Preview) Anschlussfragen stellen, um eine flüssigere Interaktion zu ermöglichen. Die \u003Cb\u003EIntegration mit Cortex Search\u003C/b\u003E (Public Preview) verbessert die Genauigkeit generierter SQL-Abfragen, indem exakte oder ähnliche Literal-Werte für komplexe, kardinalitätsintensive Datenfelder dynamisch abgerufen werden. Gleichzeitig stärken \u003Cb\u003Edie rollenbasierten Zugriffskontrollen auf\u003C/b\u003E API-Ebene die Sicherheit und Governance. \u003C/p\u003E\n\u003Cp\u003EDiese Updates ermöglichen es Unternehmen, auf sichere Weise genaue und aktuelle Erkenntnisse aus ihren Daten zu gewinnen und so die Gesamtkosten für eine datengestützte Entscheidungsfindung zu senken. Weitere Informationen zu diesen neuen Funktionen und den damit verbundenen Updates finden Sie in unserem \u003Ca href=\"https://medium.com/@sri.chintala/e77632d99739?source=friends_link&sk=edcb3728bf92f1d9cfa2947814032ba4\" target=\"_blank\"\u003ECortex Analyst Blogbeitrag\u003C/a\u003E.\u003C/p\u003E\n\u003Ch3\u003EModerne Orchestrierungs- und Beobachtbarkeitstools für LLM-Apps\u003C/h3\u003E\n\u003Cp\u003EReduzieren Sie manuelle Integration und Orchestrierung in Chatanwendungen mit der \u003Cb\u003ECortex Chat API\u003C/b\u003E (demnächst in Public Preview), die die Entwicklung interaktiver Anwendungen in Snowflake vereinfacht. Durch die Kombination von Abruf und Generierung in einem einzigen API-Aufruf können Sie jetzt agentische Chat-Apps entwickeln, um sowohl mit strukturierten als auch mit unstrukturierten Daten zu sprechen. Der optimierte Prompt ermöglicht hochwertige Antworten und Zitate, die Halluzinationen reduzieren und das Vertrauen stärken. Ein einziger Integrationsendpunkt vereinfacht die Anwendungsarchitektur. \u003C/p\u003E\n\u003Cp\u003ESteigern Sie die Vertrauenswürdigkeit von KI-Apps mit integrierter Auswertung und Überwachung durch die neue integrierte \u003Cb\u003EKI-Beobachtbarkeit für LLM-Apps\u003C/b\u003E (Private Preview). Diese Beobachtbarkeitssuite bietet wesentliche Tools, um die Bewertung und das Vertrauen in LLM-Anwendungen zu stärken und so die KI-Compliance der Kunden zu unterstützen. Dank dieser Beobachtbarkeitsfunktionen können App-Entwickler:innen während des gesamten Entwicklungsprozesses neben herkömmlichen Performance-Metriken wie Latenz auch Qualitätsmetriken wie Relevanz, Fundiertheit und Bias bewerten. Darüber hinaus ermöglichen sie eine gründliche Überwachung von Anwendungsprotokollen, sodass Unternehmen ihre KI-Anwendungen genau im Blick behalten können. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_372176798":{"id":"image-22c214237b","height":"1285","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8ea31cab-7019-41aa-a28b-96a922b28694/build2024-ai-ml-observability.png?preferwebp=true&quality=85","alt":"Three layered screenshots showing Snowflake ML Observability capability","lazyEnabled":true,"width":"2048","title":"Abbildung 2: Integrierte KI-Beobachtbarkeit für LLMs ist in Private Preview für die Entwicklung hochwertiger KI-Apps verfügbar.",":type":"snowflake-site/components/image"},"blog_text_1486098648":{"id":"blog-text-41a5f4c868","text":"\u003Cp\u003EKI-Entwickler:innen können nun die Performance-Metriken von Apps nahtlos verfolgen und auswerten und so die für ihre spezifischen Anwendungsfälle optimierten Modelle, Prompts und Abrufdienste auswählen. Darüber hinaus können Entwickler:innen Protokolle verwalten und vorgefertigtes Monitoring für Apps innerhalb von Snowflake oder für externe Apps mit der \u003Ca href=\"https://www.trulens.org/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETruLens Open Source-Bibliothek\u003C/a\u003E nutzen, die Snowflake im Rahmen der \u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-acquires-truera-to-bring-llm-ml-observability-to-data-cloud/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETruEra-Übernahme\u003C/a\u003E überwacht.\u003C/p\u003E\n\u003Ch3\u003ENeue Datenquellenintegrationen\u003C/h3\u003E\n\u003Cp\u003EBringen Sie KI mit neuen Datenquellenintegrationen zu mehr Daten. Mit dem \u003Cb\u003E\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSTZTP0KMY/snowflake-snowflake-connector-for-sharepoint\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake Connector für SharePoint\u003C/a\u003E\u003C/b\u003E (Public Preview) können Datenteams KI-Anwendungen auf Grundlage von SharePoint-Daten in Snowflake entwickeln, ohne dass sie Pipelines manuell einrichten oder Daten vorverarbeiten müssen, während sie bestehende Zugriffsrichtlinien einhalten. \u003C/p\u003E\n\u003Cp\u003EAußerdem können Sie jetzt Chatbot-Funktionen mit \u003Cb\u003ECortex Knowledge Extensions\u003C/b\u003E (Private Preview) im Snowflake Marketplace erweitern. Mit diesen Erweiterungen können Datenteams KI-Chatbots in Unternehmen mit aktuellen und proprietären Inhalten von Drittanbietern wie z. B. Forschungs- oder Zeitungspublikationen anreichern. Für Publisher und Inhaltsanbieter eröffnet sich dadurch eine neue Einnahmequelle und gleichzeitig wird geistiges Eigentum vor unbefugter Nutzung geschützt, beispielsweise für LLM-Training. Und für Verbraucher:innen bietet sie einen schnelleren Zugriff auf hochwertige KI-Antworten, ohne Bedenken über Qualität oder kommerzielle Compliance.\u003C/p\u003E\n\u003Ch2\u003EBeschleunigen Sie zuverlässige ML-Einblicke mit GPUs\u003C/h2\u003E\n\u003Cp\u003EWährend Unternehmen immer mehr Daten in verschiedensten Formaten anhäufen und die Modellierungstechniken immer ausgefeilter werden, werden die Aufgaben von Data Scientists und ML Engineers immer komplexer. \u003Ca href=\"https://www.snowflake.com/en/data-cloud/snowflake-ml/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake ML\u003C/a\u003E bietet Data-Science- und ML-Teams die nötigen Bausteine, um Features und Modelle schnell vom Prototyp zur Produktion zu bringen – auf derselben Plattform, die sie auch zur Verwaltung ihrer Daten verwenden. Unternehmen wie \u003Ca href=\"https://www.snowflake.com/webinars/customer-webinars/show-me-your-architecture-technical-deep-dive-of-chg-healthcares-ml-and-gen-ai-strategy-for-stronger-customer-relationship-management-2024-07-31/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECHG Healthcare\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/webinars/thought-leadership/building-a-personalized-chatbot-app-with-rag-and-snowflake-feature-store-2024-09-17/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStride\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/customers/all-customers/case-study/igs-energy/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EIGS Energy\u003C/a\u003E und \u003Ca href=\"https://medium.com/snowflake/from-zero-to-value-unlocking-rapid-insights-with-an-end-to-end-ml-workflow-in-snowflake-9b000a25723c\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECooke Aquaculture\u003C/a\u003E entwickeln End-to-End-ML-Modelle direkt in Snowflake. Wir haben kürzlich neue Innovationen für die Entwicklung und Bereitstellung von ML-Modellen mit verteilten GPUs für fortgeschrittene Anwendungsfälle wie \u003Ca href=\"https://quickstarts.snowflake.com/guide/getting-started-with-running-distributed-pytorch-models-on-snowflake/?_ga=2.109862725.1770139711.1729530235-871656794.1699564848&_gac=1.86430442.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EEmpfehlungssysteme\u003C/a\u003E, \u003Ca href=\"https://quickstarts.snowflake.com/guide/defect_detection_using_distributed_pyTorch_with_snowflake_notebooks/index.html?index=..%2F..index&_ga=2.109862725.1770139711.1729530235-871656794.1699564848&_gac=1.86430442.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EComputer Vision\u003C/a\u003E, \u003Ca href=\"https://quickstarts.snowflake.com/guide/scale-embeddings-with-snowflake-notebooks-on-container-runtime/index.html?index=..%2F..index&_ga=2.109862725.1770139711.1729530235-871656794.1699564848&_gac=1.86430442.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Ebenutzerdefiniterte Einbettungen\u003C/a\u003E und \u003Ca href=\"https://quickstarts.snowflake.com/guide/train-an-xgboost-model-with-gpus-using-snowflake-notebooks/?_ga=2.114671431.1770139711.1729530235-871656794.1699564848&_gac=1.27590350.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EEntscheidungsbaummodelle\u003C/a\u003E angekündigt. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_2080796899":{"id":"image-d01c71f295","height":"1087","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c14bd3fb-79e3-4fe1-b8e1-3fda0bdb5ff4/build2024-ai-ml-snowflake-ml.png?preferwebp=true&quality=85","alt":"Diagram of Snowflake ML Capabilities","lazyEnabled":true,"width":"2564","title":"Abbildung 3: Snowflake ML optimiert die Modellentwicklung und MLOps auf einer einzigen Plattform. ",":type":"snowflake-site/components/image"},"blog_text_1084608810":{"id":"blog-text-13201c0adf","text":"\u003Ch3\u003EBeschleunigen Sie die ML-Entwicklung mit GPU-gestützten Notebooks\u003C/h3\u003E\n\u003Cp\u003EGPUs bieten leistungsstarke Rechenleistung, die ressourcenintensive ML-Aufgaben wie Modelltraining beschleunigt. Diese beschleunigte Rechenleistung verbessert die Iterations- und Bereitstellungsgeschwindigkeit von Modellen für Teams erheblich, insbesondere bei der Arbeit mit großen Datasets oder der Verwendung fortschrittlicher Deep-Learning-Frameworks wie PyTorch. Snowflake ML unterstützt jetzt \u003Ca href=\"https://www.snowflake.com/en/blog/container-runtime-GPU-training-inference/\" target=\"_blank\"\u003E\u003Cb\u003EContainer Runtime\u003C/b\u003E\u003C/a\u003E (in Public Preview auf AWS und demnächst in Public Preview auf Azure), die über \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks\" target=\"_blank\"\u003E\u003Cb\u003ESnowflake Notebooks\u003C/b\u003E\u003C/a\u003E (allgemein verfügbar) verfügbar ist. Dabei handelt es sich um eine einheitliche, zellbasierte, interaktive Entwicklungsoberfläche, die Python, SQL und Markdown kombiniert. \u003C/p\u003E\n\u003Cp\u003ENach internen Tests haben wir herausgefunden, dass Snowflake ML APIs in Container Runtime ML-Trainingsjobs auf GPUs effizient ausführen können – mit einer \u003Ca href=\"https://www.snowflake.com/engineering-blog/machine-learning-container-runtime/\" target=\"_blank\"\u003E3-7-fachen Verbesserung der Ausführungsgeschwindigkeit\u003C/a\u003E im Vergleich zum Ausführen desselben Workloads mit Ihren Snowflake-Daten mithilfe von Open-Source-Bibliotheken außerhalb der Laufzeitumgebung. Diese vollständig verwaltete, containerbasierte Laufzeitumgebung ist mit den beliebtesten Python-Bibliotheken und -Frameworks vorkonfiguriert und bietet die Flexibilität, Open-Source-Hubs wie PyPI und Hugging Face zu nutzen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"youtube":{"id":"embed-15ef8d6728","youtubeVideoId":"MD5aP0uEAps?si=YBYnF2S0RSseWpC7","layout":"fixed","youtubeWidth":"840","youtubeHeight":"422","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":true,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"blog_text_531091591":{"id":"blog-text-315e2e7ffb","text":"\u003Ch3\u003ESkalieren Sie Inferenz in Containern mit GPUs\u003C/h3\u003E\n\u003Cp\u003ENach der Entwicklung können Sie Modelle aus der Snowflake Model Registry für jedes ML-, LLM- oder Einbettungsmodell mit verteilten CPUs oder GPUs in \u003Ca href=\"https://www.snowflake.com/en/blog/secure-app-deployment-snowpark-container-services-ga/\" target=\"_blank\"\u003E\u003Cb\u003ESnowpark Container Services\u003C/b\u003E\u003C/a\u003E (allgemein verfügbar in Azure und AWS) zur Produktion bereitstellen. Die \u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/container-runtime-GPU-training-inference/\" target=\"_blank\"\u003EModellbereitstellung in Containern\u003C/a\u003E\u003C/b\u003E (Public Preview in AWS) ermöglicht eine schnellere und leistungsstärkere Inferenz mit On-Demand-GPU-Instanzen, ohne dass die Ressourcennutzung manuell optimiert werden muss.\u003C/p\u003E\n\u003Ch3\u003ESchnelle Erkennung von Modellverschlechterung mit integriertem Monitoring\u003C/h3\u003E\n\u003Cp\u003EDamit ML-Modelle auch in der Produktion inferenzfähig sind, können Sie die erweiterten Funktionen von Snowflake für nativ integrierte maschinelle Lernoperationen (Machine Learning Operations, MLOps) nutzen, darunter auch die \u003Ca href=\"https://docs.snowflake.com/en/alias/snowflake-ml/ml-observability\" target=\"_blank\"\u003E\u003Cb\u003EBeobachtbarkeit für ML-Modelle\u003C/b\u003E\u003C/a\u003E (Public Preview). Teams können nun Verschlechterung, Drift und andere Modellmetriken direkt über integrierte Dashboards, die mit der Snowflake Model Registry verbunden sind, verfolgen, Warnungen festlegen und ansprechen. Ebenfalls in der Plattform integriert ist die \u003Ca href=\"https://medium.com/snowflake/snowflake-ml-explainability-now-in-public-preview-f9e65f9d4d44\" target=\"_blank\"\u003E\u003Cb\u003EML Explainability\u003C/b\u003E\u003C/a\u003E (Public Preview), mit der Benutzer:innen ganz einfach Shapley-Werte für Modelle berechnen können, die in der Snowflake Model Registry angemeldet sind – unabhängig davon, ob sie intern oder extern trainiert wurden. \u003C/p\u003E\n\u003Cp\u003EDiese neuen ML-Überwachungsfunktionen ergänzen die in Snowflake ML verfügbaren \u003Ca href=\"https://www.snowflake.com/webinars/thought-leadership/unified-and-governed-mlops-in-snowflake-2024-04-30/\" target=\"_blank\"\u003EMLOps-Funktionen\u003C/a\u003E wie \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\" target=\"_blank\"\u003E\u003Cb\u003EModel Registry\u003C/b\u003E\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/ml-lineage\" target=\"_blank\"\u003E\u003Cb\u003EML Lineage\u003C/b\u003E\u003C/a\u003E (in Public Preview) und \u003Ca href=\"https://www.snowflake.com/en/blog/build-manage-production-ml-snowflake-feature-store/\" target=\"_blank\"\u003E\u003Cb\u003EFeature Store\u003C/b\u003E\u003C/a\u003E (allgemein verfügbar).  \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_79610950":{"id":"image-d9f4e21290","height":"919","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--cb3fb17d-4610-44da-a97a-1f6a1cff3c6a/build2024-ai-ml-model-registry.png?preferwebp=true&quality=85","alt":"Screenshot of Snowflake Model Registry monitoring","lazyEnabled":true,"width":"1276","title":"Abbildung 4: Benutzer:innen können die Performance von ML-Modellen in der Produktion direkt über die Snowsight-Benutzeroberfläche in Integration mit der Snowflake Model Registry überwachen.",":type":"snowflake-site/components/image"},"blog_text_661965933":{"id":"blog-text-5dd998b9dc","text":"\u003Ch2\u003EDas volle Potenzial von Unternehmensdaten nutzen mit KI-Agenten in Snowflake Intelligence\u003C/h2\u003E\n\u003Cp\u003ESnowflake Intelligence (demnächst in Private Preview) ist eine Plattform zur Erstellung von Datenagenten, mit denen geschäftliche Benutzer:innen strukturierte und unstrukturierte Daten analysieren, zusammenfassen und entsprechende Maßnahmen ergreifen können – alles in einer einheitlichen, dialogorientierten Oberfläche. Mit Snowflake Intelligence können Benutzer:innen nahtlose Verbindungen zu Unternehmensdaten herstellen, z. B. zu Verkaufstransaktionen, Dokumenten in Wissensdatenbanken wie SharePoint und Produktivitätstools wie Jira und Google Workspace. So können geschäftliche Benutzer:innen datengestützte Einblicke gewinnen und Maßnahmen ergreifen, die vollständig in natürlicher Sprache erfolgen, ohne dass technische Kenntnisse oder Programmierkenntnisse erforderlich sind.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_460253025":{"id":"image-b70cdfafa0","height":"473","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8a323973-57f9-46dd-b20d-bc8774d88dc6/build2024-ai-ml-snowflake-intelligence1.gif?preferwebp=true&quality=85","alt":"Gif animation showing how Snowflake Intelligence works","lazyEnabled":true,"width":"925",":type":"snowflake-site/components/image"},"blog_text_1553733623":{"id":"blog-text-52379fa814","text":"\u003Ch1\u003EMehr erfahren\u003C/h1\u003E\n\u003Cp\u003EMit den neuesten Verbesserungen in \u003Ca href=\"https://www.snowflake.com/de/data-cloud/cortex/\" target=\"_blank\"\u003ECortex AI\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/en/data-cloud/snowflake-ml/\" target=\"_blank\"\u003ESnowflake ML\u003C/a\u003E können Entwickler:innen GenAI-Apps zuverlässig in der sicheren Umgebung von Snowflake produzieren.\u003C/p\u003E\n\u003Cp\u003ENutzen Sie diese Ressourcen, um noch heute KI-Apps und individuelle Modelle zu entwickeln:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ENLP-Lösungen für Daten-Pipelines: \u003C/b\u003ETesten Sie unseren Quickstart für \u003Ca href=\"https://quickstarts.snowflake.com/guide/customer_reviews_analytics_using_snowflake_cortex/index.html?index=..%2F..index#0\" target=\"_blank\"\u003EAnalytics zu Kundenrezensionen mit Snowflake Cortex\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDialogorientierte Apps:\u003C/b\u003E Entwickeln Sie mithilfe unseres \u003Ca href=\"https://quickstarts.snowflake.com/guide/ask_questions_to_your_own_documents_with_snowflake_cortex_search/index.html?index=..%2F..index#0\" target=\"_blank\"\u003EQuickstarts\u003C/a\u003E einen RAG-basierten Chatbot oder nehmen Sie am \u003Ca href=\"http://snowflake-mistral-rag.devpost.com\" target=\"_blank\"\u003ERAG ’n’ Roll Hackathon\u003C/a\u003E teil und gewinnen Sie 10.000 USD an Preisen. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGPU-basiertes ML:\u003C/b\u003E Beginnen Sie mit dem \u003Ca href=\"https://quickstarts.snowflake.com/guide/train-an-xgboost-model-with-gpus-using-snowflake-notebooks/?_ga=2.114671431.1770139711.1729530235-871656794.1699564848&_gac=1.27590350.1729530237.Cj0KCQjw99e4BhDiARIsAISE7P8pLzCX0R9zUEdH5SJWnwQCZrffTyvNmgbSqJ5uG7j_w_SjXkJUBQsaAuWfEALw_wcB&_fsi=hnlih63N#0\" target=\"_blank\"\u003ETraining eines XGBoost-Modells\u003C/a\u003E oder \u003Ca href=\"https://quickstarts.snowflake.com/guide/scale-embeddings-with-snowflake-notebooks-on-container-runtime/index.html?index=..%2F..index#0\" target=\"_blank\"\u003Eskalieren Sie benutzerdefinierte Einbettungen\u003C/a\u003E aus Snowflake Notebooks.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Ci style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003EHinweis: \u003Ci style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\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\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_10630711","image_372176798","blog_text_1486098648","image_2080796899","blog_text_1084608810","youtube","blog_text_531091591","image_79610950","blog_text_661965933","image_460253025","blog_text_1553733623"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"premium_content_bann":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{"premium_content_bann":{"id":"premium-content-banner-00a697fcf4","contentEyebrow":"Ebook","contentHeadline":"Secrets of Gen AI Success","contentDescription":"Discover how leaders like Bayer and Siemens 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