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Erfahren Sie, wie prädiktive Modelle funktionieren, wie Teams sie evaluieren und pflegen und wie prädiktive KI diese Techniken in großem Maßstab erweitert.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_1934028918":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-36df072bc5",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-19706a0b35","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Biografie 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Handel\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-8c5c3263ba","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"callout__0":{"id":"text-dc91501b2b","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EPREDICTIVE ANALYTICS – DEFINITION\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EPredictive Analytics (prädiktive Analytik) ist die Praxis, vorhandene Daten zu nutzen, um zukünftige Werte, Ereignisse oder Wahrscheinlichkeiten durch statistische Modellierung und maschinelles Lernen abzuschätzen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-c9c45d62c0","text":"\u003Cp\u003EPredictive Analytics beginnt mit einer grundlegenden Annahme: Muster in historischen Daten verraten uns etwas Nützliches darüber, was als Nächstes passiert.\u003C/p\u003E\n\u003Cp\u003EOft tun sie das auch. Aber das Kundenverhalten ändert sich, die Betriebsbedingungen verschieben sich und Risikomuster entwickeln sich weiter. Der wahre Test eines prädiktiven Modells erfolgt nach der Bereitstellung, wenn es auf Bedingungen trifft, die sich von denen in seinen Trainingsdaten unterscheiden. Lassen sich die aus historischen Daten gelernten Muster gut genug verallgemeinern, um Schätzungen für neue Fälle zu unterstützen?\u003C/p\u003E\n\u003Cp\u003EDieses Spannungsfeld steht im Mittelpunkt von Predictive Analytics: Modelle lernen aus der Vergangenheit, werden aber nach der Zukunft beurteilt. Das Prinzip gilt unabhängig davon, ob die Methode klassische Statistik oder prädiktive KI ist. Predictive Analytics funktioniert am besten, wenn Unternehmen Vorhersagen als Schätzungen behandeln, die im Laufe der Zeit getestet und gepflegt werden müssen, und nicht als feste Antworten darauf, was als Nächstes kommt.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-predictive-analytics":{"id":"title-v2-936a9fb502","additionalClasses":"anchor-title anchor-title--what-is-predictive-analytics","type":"heading2","lines":["Was ist Predictive Analytics?"],":type":"snowflake-site/components/title-v2"},"text_what-is-predictive-analytics_0":{"id":"text-74cd42699f","text":"\u003Cp\u003EPredictive Analytics nutzt historische Daten, statistische Techniken und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/\"\u003Emaschinelles Lernen\u003C/a\u003E (ML), um die Wahrscheinlichkeit eines zukünftigen Ergebnisses abzuschätzen. Je nach Anwendungsfall kann dieses Ergebnis eine Wahrscheinlichkeit, eine Prognose oder ein Risikowert sein. In jedem Fall drückt es aus, was wahrscheinlich passieren wird, und nicht, was mit Sicherheit passieren wird.\u003C/p\u003E\r\n\u003Cp\u003EDiese zukunftsorientierte Ausrichtung unterscheidet Predictive Analytics von den Formen der Analytics, die sich auf bereits eingetretene Ereignisse konzentrieren. Deskriptive Analytik erklärt, was passiert ist, während diagnostische Analytik untersucht, warum. Predictive Analytics fragt, was als Nächstes wahrscheinlich passieren wird, und Prescriptive Analytics (präskriptive Analytik) nutzt diese Informationen dann, um eine Reaktion oder Aktion zu steuern.\u003C/p\u003E\r\n\u003Cp\u003EPrädiktive Systeme haben zusätzliche Anforderungen, die über die von deskriptiven und diagnostischen Systemen hinausgehen. Historische Daten müssen die Bedingungen repräsentieren, auf die das Modell treffen wird, Vorhersagen müssen anhand von Ergebnissen validiert werden, die das Modell noch nicht gesehen hat, und die Performance muss überwacht werden, wenn sich diese Bedingungen ändern. Ein Modell, das beispielsweise letztes Jahr gut funktioniert hat, könnte allmählich an Genauigkeit verlieren, wenn sich Kundenverhalten, Marktbedingungen oder Betriebsmuster ändern.\u003C/p\u003E\r\n\u003Cp\u003EML hat erweitert, wie umfassend Unternehmen prädiktive Techniken anwenden können. Statistische Prognosen und Klassifizierungen sind älter als modernes ML, aber maschinelles Lernen macht es praktikabel, prädiktive Modelle über sehr große Populationen von Kund:innen, Produkten, Transaktionen oder anderen Entitäten hinweg auszuführen.\u003C/p\u003E\r\n\u003Cp\u003EPredictive Analytics und prädiktive KI überschneiden sich stark, aber die Begriffe beschreiben leicht unterschiedliche Dinge. Predictive Analytics ist die analytische Disziplin, historische Muster zu nutzen, um zukünftige Ergebnisse abzuschätzen. Prädiktive KI bezieht sich im Allgemeinen auf die Anwendung von ML-Modellen, um diese Vorhersagen in großem Maßstab durchzuführen.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EIn dieser Folge von „AI Builders“ diskutiert Dan Darnell, VP of Marketing bei Sway AI, wie man eine No-Code-Umgebung schafft, die es Unternehmen jeder Größe erleichtert, ML und Predictive Analytics zu nutzen:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-predictive-analytics_0":{"id":"embed-f94e3758fa","youtubeVideoId":"fRXW-NaAjO4","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","isLcpImage":false,"type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_how-predictive-analytics-works":{"id":"title-v2-af435b8b47","additionalClasses":"anchor-title anchor-title--how-predictive-analytics-works","type":"heading2","lines":["Wie Predictive Analytics funktioniert"],":type":"snowflake-site/components/title-v2"},"text_how-predictive-analytics-works_0":{"id":"text-94dd17f8ac","text":"\u003Cp\u003EPredictive Analytics folgt einem Lebenszyklus, aber die Phasen sind eng miteinander verbunden. Entscheidungen, die früh im Prozess getroffen werden, prägen alles, was danach kommt. Der typische Lebenszyklus sieht so aus:\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cb\u003EEntscheidung festlegen:\u003C/b\u003E Definieren Sie das Ergebnis, den Vorhersagehorizont und die mit dem Ergebnis verbundene Aktion.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EHistorische Daten zusammenstellen:\u003C/b\u003E Führen Sie relevante Quellen zusammen und bewerten Sie deren Vollständigkeit und Konsistenz.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EFeatures entwickeln:\u003C/b\u003E Transformieren Sie rohe Beobachtungen in nützliche Signale für Training und Inferenz.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EModell auswählen und trainieren:\u003C/b\u003E Wählen Sie einen Ansatz, der für die Vorhersage und die verfügbaren Daten geeignet ist.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EModell validieren:\u003C/b\u003E Testen Sie anhand von zurückgehaltenen Daten mit Metriken, die das Geschäftsziel widerspiegeln.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EVorhersage-Workflow bereitstellen:\u003C/b\u003E Speisen Sie aktuelle Daten in das Modell ein und leiten Sie dessen Ausgabe an die Personen oder Systeme weiter, die sie nutzen.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EÜberwachen und neu trainieren:\u003C/b\u003E Verfolgen Sie die Performance und aktualisieren Sie das Modell, wenn sich zugrunde liegende Muster ändern.\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Ch3\u003EBeginnen Sie mit der Vorhersage, die Sie treffen müssen\u003C/h3\u003E\r\n\u003Cp\u003EDer erste Schritt besteht darin, das Ergebnis klar genug zu definieren, damit das Modell etwas Spezifisches abschätzen kann. Dazu gehören das Vorhersageziel, der Zeithorizont und der Kontext, in dem das Ergebnis verwendet wird. Die Vorhersage auf diese Weise zu formulieren, hält das Projekt auch an eine reale Geschäftsfrage gebunden.\u003C/p\u003E\r\n\u003Cp\u003EEin Churn-Modell benötigt beispielsweise mehr als das allgemeine Ziel, Kund:innen zu identifizieren, die abwandern könnten. Ist das Ziel die Abwanderung innerhalb von 30 Tagen? 90 Tagen? Vor der nächsten Vertragsverlängerung?\u003C/p\u003E\r\n\u003Ch3\u003EDie historische Sicht aufbauen\u003C/h3\u003E\r\n\u003Cp\u003ESobald das Vorhersageziel klar ist, stellen Teams die damit verbundenen historischen Daten zusammen. Dies bedeutet in der Regel, Datensätze aus mehreren Quellen zusammenzuführen, Inkonsistenzen zu bereinigen und Features zu erstellen, die die Muster repräsentieren, die das Modell lernen soll.\u003C/p\u003E\r\n\u003Cp\u003EDiese Phase kann viel Zeit in Anspruch nehmen, wenn wichtige Variablen in getrennten Systemen liegen, wenn sich Definitionen geändert haben oder wenn fehlende oder inkonsistente Datensätze vorhanden sind, die die vom Modell gelernten Beziehungen verzerren können.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.sciencedirect.com/science/article/pii/S2095809924003734\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EAktuelle Forschung zum industriellen maschinellen Lernen\u003C/a\u003E weist auf Datenqualität, -knappheit, -ungleichgewicht und -heterogenität als wesentliche Produktionseinschränkungen hin. Das Modell hat nur so viel Signal zur Verfügung, wie die Trainingsdaten liefern können.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/feature-engineering/\"\u003EFeature Engineering\u003C/a\u003E ist ein wichtiger Teil dieses Prozesses. Rohe Beobachtungen werden in Signale transformiert, die für die Vorhersage nützlicher sind – beispielsweise die Umwandlung einer Reihe von Transaktionen in Aktualitäts-, Häufigkeits- oder Trend-Features oder die Darstellung historischer Werte als verzögerte Features in einem Zeitreihenmodell.\u003C/p\u003E\r\n\u003Ch3\u003EDas Modell trainieren und validieren\u003C/h3\u003E\r\n\u003Cp\u003EWenn die Trainingsdaten vorbereitet sind, wählen Teams eine geeignete Modellfamilie aus und trainieren sie mit einem Teil der historischen Daten. Nach dem \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/model-training/\"\u003EModelltraining\u003C/a\u003E ist der nächste Schritt die Validierung anhand von zurückgehaltenen Daten, die das Modell noch nicht gesehen hat. Die Trennung dieser Schritte gibt einen besseren Hinweis darauf, ob sich die während des Trainings gelernten Muster auf neue Fälle verallgemeinern lassen, anstatt sich lediglich an die historische Stichprobe anzupassen.\u003C/p\u003E\r\n\u003Cp\u003EDie für die \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/model-evaluation/\"\u003EBewertung\u003C/a\u003E verwendeten Metriken sollten die Art der getroffenen Vorhersage widerspiegeln. Die Gesamtgenauigkeit kann beispielsweise irreführend sein, wenn ein Ergebnis selten ist. Bei der Betrugserkennung könnte ein Modell, das fast jede Transaktion als legitim einstuft, einen hohen Genauigkeitswert erzielen, während es die Fälle übersieht, die dem Unternehmen am wichtigsten sind. Precision, Recall oder andere aufgabenspezifische Maße können in diesen Fällen eine nützlichere Sicht bieten.\u003C/p\u003E\r\n\u003Ch3\u003EDas Modell in Produktion bringen\u003C/h3\u003E\r\n\u003Cp\u003EEin validiertes Modell benötigt einen zuverlässigen Weg, um aktuelle Daten in nutzbare Vorhersagen zu verwandeln. Features müssen konsistent generiert, planmäßig aktualisiert und in derselben Form an das Modell übergeben werden, die während des Trainings verwendet wurde – andernfalls kann ein \u003Ca href=\"https://nhimg.org/glossary/train-serving-skew/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETraining-Serving-Skew\u003C/a\u003E die Performance beeinträchtigen.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/inference/\"\u003EInferenz\u003C/a\u003E benötigt ebenfalls einen definierten Rhythmus und ein definiertes Ziel. Eine Bedarfsprognose könnte nächtlich laufen, während ein Betrugsmodell Transaktionen nahezu in Echtzeit bewerten kann. Diese zeitlichen Anforderungen prägen die Serving-Architektur.\u003C/p\u003E\r\n\u003Cp\u003EProduktions-Workflows sollten zudem reproduzierbar und nachvollziehbar sein. Teams müssen wissen, welche Modellversion eine Vorhersage erstellt hat, welche Daten und Feature-Logik sie gespeist haben und wann sie zuletzt neu trainiert wurde.\u003C/p\u003E\r\n\u003Ch3\u003EÜberwachen, was sich ändert\u003C/h3\u003E\r\n\u003Cp\u003EDie Bereitstellung ist nicht das Ende des Lebenszyklus. Die prädiktive Performance hängt davon ab, dass die aus historischen Daten gelernten Beziehungen weiterhin Bestand haben.\u003C/p\u003E\r\n\u003Cp\u003EIm Laufe der Zeit können sich eingehende Daten verschieben, die Beziehung zwischen einer Eingabe und einem Ergebnis kann schwächer werden oder es können völlig neue Muster auftreten. \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/observability/model-monitoring/\"\u003EMonitoring\u003C/a\u003E hilft dabei, diese Änderungen aufzudecken, bevor sich eine nachlassende Performance in der Vorhersage selbst zeigt.\u003C/p\u003E\r\n\u003Cp\u003EDurch erneutes Training kann das Modell neuere Informationen integrieren. Je nach Anwendungsfall können Teams nach einem Zeitplan, als Reaktion auf Drift oder wenn die Performance unter einen definierten Schwellenwert fällt, neu trainieren.\u003C/p\u003E\r\n\u003Cp\u003EEine \u003Ca href=\"https://www.sciencedirect.com/science/article/abs/pii/S1532046425001315\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EÜbersichtsstudie zu prädiktiven ML-Systemen aus dem Jahr 2025\u003C/a\u003E ergab, dass zeitliche Verschiebungen und Concept-Drift häufige Ursachen für Änderungen nach der Bereitstellung sind. Die praktische Realität ist, dass Muster, die ein Modell während des Trainings gelernt hat, oft neu überprüft werden müssen, sobald sich die Umgebung ändert.\u003C/p\u003E\r\n\u003Ch3\u003EEntscheiden Sie, wie detailliert das Modell sein soll\u003C/h3\u003E\r\n\u003Cp\u003EDie Skalierung führt zu einer weiteren Designentscheidung: wie viele separate Modelle verwaltet werden sollen. Ein Modell pro Kunde, Produkt oder Standort erfasst möglicherweise mehr lokale Variationen, aber jedes zusätzliche Modell bringt seine eigenen Anforderungen an Training, Monitoring und erneutes Training mit sich. In großem Maßstab kann dies Hunderte oder Tausende von parallelen Workflows erzeugen, die betrieben werden müssen.\u003C/p\u003E\r\n\u003Cp\u003EEin einheitliches Modell, das über viele Entitäten hinweg trainiert wurde, reduziert diesen operativen Fußabdruck, wenn es dennoch die vom Anwendungsfall geforderte Variation erfasst. Der richtige Grad an Granularität hängt daher sowohl von der prädiktiven Performance als auch von den Kosten für die Aufrechterhaltung des Modellbestands im Laufe der Zeit ab.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-predictive-analytics-works_0":{"id":"card-v2-60180cd871","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/fireblocks/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Customer Story lesen (Englisch)"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer Story: Fireblocks"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","height":"234","src":"/adobe/dynamicmedia/deliver/dm-aid--e1f97c5a-173b-4371-b4cc-f55ee9564d67/fireblocks.png?preferwebp=true&quality=85","lazyEnabled":true,"alt":"Fireblocks logo","isLcpImage":false,"width":"416",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003E\u003Cu\u003E\u003C/u\u003EFireblocks nutzt Cortex Agents und Semantic Views, um mehr als 200 Mitarbeitenden natürlichsprachlichen Zugriff auf Analytics zu geben. Sein KI-Agent verarbeitet jetzt mehr als 2.000 Analyseabfragen pro Monat – etwa 40 % bis 50 % aller Abfragen – und setzt gleichzeitig das Äquivalent von zwei Vollzeit-Analyst:innen für höherwertige Arbeiten frei.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_types-of-predictive-analytics-models":{"id":"title-v2-17912f0084","additionalClasses":"anchor-title anchor-title--types-of-predictive-analytics-models","type":"heading2","lines":["Arten von Modellen für Predictive Analytics"],":type":"snowflake-site/components/title-v2"},"text_types-of-predictive-analytics-models_0":{"id":"text-f8ad296c01","text":"\u003Cp\u003EDie Modellauswahl wird einfacher, sobald die prädiktive Frage klar ist. Die meisten Probleme von Predictive Analytics fallen in eine kleine Anzahl von Familien, die jeweils einer anderen Art von Frage entsprechen:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EKlassifizierung:\u003C/b\u003E Zu welcher Kategorie gehört diese Beobachtung?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ERegression:\u003C/b\u003E Welchen numerischen Wert sollten wir erwarten?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EClustering:\u003C/b\u003E Welche natürlichen Gruppen existieren in den Daten?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EZeitreihen:\u003C/b\u003E Was wird wahrscheinlich als Nächstes passieren, angesichts dessen, was zuvor passiert ist?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EEnsemblemethoden:\u003C/b\u003E Welches Ergebnis erhalten wir durch die Kombination mehrerer Modelle?\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EBeachten Sie, dass \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/\"\u003EModellfamilien\u003C/a\u003E und Modellierungstechniken unterschiedliche Dinge sind. Beispielsweise beschreibt die Klassifizierung ein Ziel, während verschiedene Techniken verwendet werden können, um es zu erreichen.\u003C/p\u003E\r\n\u003Ch3\u003EKlassifizierungsmodelle\u003C/h3\u003E\r\n\u003Cp\u003EKlassifizierungsmodelle weisen Beobachtungen Kategorien zu oder schätzen die Wahrscheinlichkeit, dass eine Beobachtung zu einer bestimmten Klasse gehört. Ein Churn-Modell kann beispielsweise die Wahrscheinlichkeit schätzen, dass ein:e Kund:in innerhalb der nächsten 90 Tage abwandert, oder ein Risikomodell kann Antragsteller:innen oder Konten in vordefinierte Kategorien einteilen.\u003C/p\u003E\r\n\u003Cp\u003ETechniken wie logistische Regression, \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/decision-tree/\"\u003EEntscheidungsbäume\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/random-forest/\"\u003ERandom Forests\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/fundamentals/what-is-gradient-boosting/\"\u003EGradient Boosting\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/neural-network/\"\u003Eneuronale Netzwerke\u003C/a\u003E unterstützen alle Klassifizierungsprobleme. Die richtige Wahl hängt teils von den Performance-Anforderungen und teils davon ab, wie viel Erklärung die Entscheidung erfordert.\u003C/p\u003E\r\n\u003Ch3\u003ERegressionsmodelle\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/regression/\"\u003ERegressionsmodelle\u003C/a\u003E schätzen kontinuierliche numerische Werte. Umsatzprognosen, der erwartete Customer Lifetime Value und Preisschätzungen sind gängige Beispiele. \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/models/linear-regression/\"\u003ELineare Regression\u003C/a\u003E bietet einen relativ transparenten Ausgangspunkt, während baumbasierte Methoden, Ensembles und neuronale Netzwerke komplexere Beziehungen unterstützen.\u003C/p\u003E\r\n\u003Cp\u003EBei einem Regressionsmodell muss ein Team wissen, welches numerische Ergebnis das Unternehmen zu schätzen versucht und wie präzise diese Schätzung für die von ihr unterstützte Entscheidung sein muss.\u003C/p\u003E\r\n\u003Ch3\u003EClustering-Modelle\u003C/h3\u003E\r\n\u003Cp\u003EClustering-Algorithmen suchen nach natürlich vorkommenden Gruppen in Daten, ohne von einem bekannten Ziel-Label auszugehen. \u003Ca href=\"https://stanford.edu/~cpiech/cs221/handouts/kmeans.html\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EK-Means-Clustering\u003C/a\u003E könnte beispielsweise Kunden identifizieren, deren Kaufmuster einander ähneln, selbst wenn das Unternehmen diese Segmente nicht vordefiniert hat. Diese Cluster können dann Segmentierung, Targeting oder weitere Analysen unterstützen.\u003C/p\u003E\r\n\u003Cp\u003EDa das Modell eher Strukturen entdeckt als ein bekanntes Ziel vorherzusagen, wird Clustering etwas anders eingesetzt als Klassifizierung und Regression. Es ist oft früher in einem analytischen Workflow nützlich, wenn Teams noch versuchen zu verstehen, welche Gruppen oder Muster existieren.\u003C/p\u003E\r\n\u003Ch3\u003EZeitreihenmodelle\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/fundamentals/time-series-analysis/\"\u003EZeitreihenmodelle\u003C/a\u003E verwenden über die Zeit geordnete Beobachtungen, um zukünftige Werte zu schätzen. Nachfrage, Umsatz, Energielast, finanzielle Performance und Lagerbestände passen alle natürlich in diese Kategorie. Saisonalität, Trend und Prognosehorizont prägen das Modelldesign, während die Techniken von statistischen Ansätzen wie \u003Ca href=\"https://www.datacamp.com/tutorial/arima\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EARIMA\u003C/a\u003E bis hin zu ML-Methoden reichen, die über mehrere verwandte Reihen hinweg trainiert wurden.\u003C/p\u003E\r\n\u003Cp\u003EDiese Familie taucht bei Predictive Analytics in Unternehmen aus einem einfachen Grund häufig auf: Viele geschäftliche Fragen sind mit einem Datum versehen. Wie viel Lagerbestand werden wir nächsten Monat benötigen? Wie wird der Umsatz im nächsten Quartal aussehen? Wie viel Energienachfrage sollten wir morgen erwarten?\u003C/p\u003E\r\n\u003Ch3\u003EEnsemblemethoden\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/ensemble-learning/\"\u003EEnsemblemethoden\u003C/a\u003E kombinieren mehrere Modelle, um die Stabilität oder die prädiktive Performance zu verbessern. Random Forests und Gradient Boosting sind bekannte Beispiele. Andere Ensembles aggregieren Ausgaben von unabhängig trainierten Modellen und reduzieren so das Ausmaß, in dem das Endergebnis von den Stärken oder Schwächen eines einzelnen Ansatzes abhängt.\u003C/p\u003E\r\n\u003Cp\u003EDie Verbesserung geht oft mit einem Kompromiss einher: Ein großes Ensemble oder neuronales Netzwerk ist im Allgemeinen weniger einfach zu erklären. In regulierten Umgebungen, in denen Prüfer:innen oder Kund:innen möglicherweise verstehen müssen, wie eine Entscheidung getroffen wurde, ist Interpretierbarkeit nicht verhandelbar.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_generative-ai-in-predictive-analytics":{"id":"title-v2-f8a9f4f1a3","additionalClasses":"anchor-title anchor-title--generative-ai-in-predictive-analytics","type":"heading2","lines":["Generative KI in Predictive Analytics"],":type":"snowflake-site/components/title-v2"},"text_generative-ai-in-predictive-analytics_0":{"id":"text-a90c6dab4d","text":"\u003Cp\u003EPrädiktive Modelle schätzen ein definiertes zukünftiges Ergebnis – wie Nachfrage, Abwanderungswahrscheinlichkeit oder Geräteausfall – anhand historischer Daten. \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/generative-ai/\"\u003EGenerative KI\u003C/a\u003E produziert neue Inhalte, einschließlich Text, Bildern oder Code, basierend auf Mustern, die während des Trainings gelernt wurden.\u003C/p\u003E\n\u003Cp\u003EDie beiden arbeiten zunehmend zusammen. Eine KI-gesteuerte Plattform für Predictive Analytics könnte beispielsweise die Ausgabe eines prädiktiven Modells erklären oder zusammenfassen, während das prädiktive Modell weiterhin die Prognose oder Wahrscheinlichkeit selbst liefert.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_predictive-analytics-tools-and-platforms":{"id":"title-v2-c8138f14b6","additionalClasses":"anchor-title anchor-title--predictive-analytics-tools-and-platforms","type":"heading2","lines":["Tools und Plattformen für Predictive Analytics"],":type":"snowflake-site/components/title-v2"},"text_predictive-analytics-tools-and-platforms_0":{"id":"text-d114515642","text":"\u003Cp\u003EEin Tool oder eine Plattform für Predictive Analytics muss den umgebenden Workflow unterstützen: wo das Training stattfindet, wo die Daten liegen, wer damit arbeiten kann und wohin die Vorhersage danach geht. Diese architektonischen Entscheidungen haben oft mehr operative Auswirkungen als der Unterschied zwischen zwei Modellierungsbibliotheken.\u003C/p\u003E\n\u003Cp\u003EBei der Evaluierung von Tools für Predictive Analytics sollten Teams fragen:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EWo trainiert das Modell im Verhältnis zu den Quelldaten mit zentraler Governance?\u003C/li\u003E\n\u003Cli\u003EErfordert der Workflow, dass Daten in eine andere Umgebung kopiert werden?\u003C/li\u003E\n\u003Cli\u003EWie erreicht eine Vorhersage eine Anwendung, ein Dashboard oder einen Geschäftsprozess?\u003C/li\u003E\n\u003Cli\u003EWelche Nutzer:innen können prädiktive Workloads erstellen oder ausführen?\u003C/li\u003E\n\u003Cli\u003EWie werden Modellversionen, Datenherkunft (Data Lineage) und erneutes Training verfolgt?\u003C/li\u003E\n\u003Cli\u003EWelche Infrastruktur ist für die Inferenz erforderlich?\u003C/li\u003E\n\u003Cli\u003EWie verändern sich die Kosten, wenn die Anzahl der Modelle oder Vorhersagen wächst?\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EDatenbewegungen verdienen besondere Aufmerksamkeit. Das Kopieren von Daten mit zentraler Governance in eine separate ML-Umgebung schafft eine weitere Version, die synchronisiert werden muss, eine weitere Pipeline, die gewartet werden muss, und eine weitere Governance-Grenze. Wenn die Kopie hinter der Quelle zurückbleibt oder eine Upstream-Pipeline ausfällt, arbeitet das Modell nicht mehr mit dem aktuellen Zustand des Unternehmens.\u003C/p\u003E\n\u003Cp\u003EAuch der Kreis der Nutzer:innen verändert sich. SQL-native prädiktive Funktionen ermöglichen es Analyst:innen und Analytics Engineers, gängige Workloads wie Prognosen und Anomalieerkennung auszuführen, ohne jede Anfrage durch eine Data-Science-Warteschlange leiten zu müssen. Benutzerdefinierte Modelle erfordern weiterhin Code-First-Umgebungen, wenn Teams spezialisiertes Feature Engineering, Algorithmen oder Evaluierungen benötigen. Zunehmend können diese beiden Ansätze koexistieren, anstatt separate Stacks für jede Art von prädiktiver Arbeit zu erfordern.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_predictive-analytics-use-cases-and-business-impact":{"id":"title-v2-d73092beb3","additionalClasses":"anchor-title anchor-title--predictive-analytics-use-cases-and-business-impact","type":"heading2","lines":["Anwendungsfälle und geschäftliche Auswirkungen von Predictive Analytics"],":type":"snowflake-site/components/title-v2"},"text_predictive-analytics-use-cases-and-business-impact_0":{"id":"text-e843372361","text":"\u003Cp\u003EBranchenlisten lassen Predictive Analytics fragmentierter erscheinen, als sie ist. Darunter reduzieren sich die meisten Anwendungsfälle auf eine Handvoll wiederkehrender Problemtypen: Prognose der zukünftigen Nachfrage, Schätzung, ob ein Ereignis eintreten wird, Identifizierung von abnormalem Verhalten, Antizipation von Geräteausfällen oder Zuweisung eines Risikoniveaus.\u003C/p\u003E\n\u003Cp\u003EBeispielsweise lösen ein Versorgungsunternehmen, das die Energielast prognostiziert, und ein Händler, der die Produktnachfrage prognostiziert, eng verwandte analytische Probleme, auch wenn die umgebenden Geschäftsprozesse völlig unterschiedlich aussehen.\u003C/p\u003E\n\u003Ch3\u003ENachfrage- und Umsatzprognosen\u003C/h3\u003E\n\u003Cp\u003EPrognosen nutzen historische Muster, Saisonalität und verwandte Signale, um zukünftige Aktivitäten zu schätzen. Der Wert zeigt sich nachgelagert zur Prognose selbst. Eine zeitnahere oder genauere Schätzung könnte ändern, wie viel Lagerbestand bestellt wird, wo Ressourcen zugewiesen werden oder auf welche Szenarien sich ein Finanzteam vorbereitet.\u003C/p\u003E\n\u003Ch3\u003EAbwanderungs- und Bindungsvorhersage\u003C/h3\u003E\n\u003Cp\u003EChurn-Modelle schätzen die Wahrscheinlichkeit, dass ein:e Kund:in ein Produkt oder eine Dienstleistung nicht mehr nutzt. Das Timing ist für den Anwendungsfall von zentraler Bedeutung. Eine Vorhersage, die geliefert wird, nachdem der:die Kund:in bereits gekündigt hat, hat beispielsweise wenig operativen Wert. Teams benötigen die Erkenntnis früh genug, um eine Kontaktaufnahme, eine Serviceintervention oder ein Bindungsangebot auszulösen. Aus diesem Grund sollten das Modell und der Geschäftsprozess gemeinsam entworfen werden.\u003C/p\u003E\n\u003Ch3\u003EBetrugs- und Anomalieerkennung\u003C/h3\u003E\n\u003Cp\u003EDie Betrugserkennung konzentriert sich auf Muster, die mit verdächtigem Verhalten in Verbindung gebracht werden, während die Anomalieerkennung nach Beobachtungen sucht, die von einer erwarteten Baseline abweichen.\u003C/p\u003E\n\u003Cp\u003EFinanzdienstleister nutzen diese Ansätze, um potenziell betrügerische Transaktionen oder Konten zu markieren. Ähnliche Techniken gelten für operative Metriken, die Performance von Geräten, Sicherheitsereignisse und andere Umgebungen, in denen eine unerwartete Abweichung eine Untersuchung rechtfertigt.\u003C/p\u003E\n\u003Cp\u003EHier ergibt sich der prädiktive Wert oft aus der Eingrenzung der Aufmerksamkeit. Anstatt jedes Ereignis gleichermaßen zu überprüfen, können Teams ihre Ressourcen auf die Teilmenge konzentrieren, die am wahrscheinlichsten Maßnahmen erfordert.\u003C/p\u003E\n\u003Ch3\u003EVorausschauende Wartung\u003C/h3\u003E\n\u003Cp\u003EHersteller und andere anlagenintensive Unternehmen nutzen Geräte-, Sensor- und Wartungsdaten, um abzuschätzen, wann Ausfälle wahrscheinlich sind. Eine frühere Warnung ermöglicht es Betreiber:innen, die Wartung zu planen, bevor ein Ausfall die Produktion unterbricht. Der geschäftliche Nutzen zeigt sich eher in Form von vermiedenen Ausfallzeiten, weniger Notfallreparaturen und einer effizienteren Nutzung von Wartungsressourcen als in zusätzlichen Einnahmen.\u003C/p\u003E\n\u003Ch3\u003ERisikobewertung\u003C/h3\u003E\n\u003Cp\u003EPrädiktive Modelle schätzen auch die Wahrscheinlichkeit von Ereignissen wie Kreditausfällen, Schadensfällen, Kundenabwanderung oder anderen Formen von Geschäftsrisiken. Ein Score bietet dem Unternehmen eine konsistente Möglichkeit, die Aufmerksamkeit zu priorisieren oder je nach erwartetem Risiko unterschiedliche Workflows anzuwenden. Wie bei den anderen Anwendungsfällen hängt der Wert davon ab, was nach der Erstellung des Scores geschieht.\u003C/p\u003E\n\u003Cp\u003EBei allen fünf Mustern ist der rote Faden, dass Predictive Analytics das Intervall zwischen dem Auftreten eines aussagekräftigen Signals in den Daten und der Reaktion darauf verkürzt.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_getting-predictive-models-into-production":{"id":"title-v2-f09b63eb01","additionalClasses":"anchor-title anchor-title--getting-predictive-models-into-production","type":"heading2","lines":["Prädiktive Modelle in die Produktion überführen"],":type":"snowflake-site/components/title-v2"},"text_getting-predictive-models-into-production_0":{"id":"text-6a23e96e87","text":"\u003Cp\u003EEin trainiertes Modell ist nur ein Teil eines prädiktiven Produktions-Workflows. Neue Features müssen es planmäßig erreichen, die Inferenz muss zuverlässig ablaufen und Vorhersagen müssen an die Systeme oder Personen geliefert werden, die sie nutzen. Teams müssen außerdem die Datenherkunft ausreichend nachvollziehen können, um eine Vorhersage bis zu der Modellversion, den Daten und der Feature-Logik zurückzuverfolgen, die sie erzeugt haben.\u003C/p\u003E\n\u003Ch3\u003ESorgen Sie für Reproduzierbarkeit\u003C/h3\u003E\n\u003Cp\u003EDie Produktion bricht schnell zusammen, wenn ein Modell von einem manuell zusammengestellten Dataset, undokumentierten Transformationen oder einem Notebook abhängt, bei dem nur die Person, die es erstellt hat, weiß, wie es ausgeführt wird. Dieselbe Datenaufbereitung und Feature-Logik, die während des Trainings verwendet wurden, müssen während der Inferenz konsistent ausgeführt werden, mit versionierten Prozessen, die andere Teams reproduzieren können. Diese Konsistenz reduziert auch den Training-Serving-Skew, bei dem die Daten oder Features, die einem bereitgestellten Modell präsentiert werden, von dem abweichen, was es während des Trainings gesehen hat.\u003C/p\u003E\n\u003Ch3\u003EAuf Skalierbarkeit auslegen\u003C/h3\u003E\n\u003Cp\u003EDie Anzahl der Modelle wirkt sich direkt auf die Betriebskosten aus. Ein separates Modell für jeden Kunden, jede SKU oder jeden Standort vervielfacht Trainingsjobs, Modellversionen, Monitoring und Retraining-Zeitpläne. In einigen Fällen kann ein einheitliches Modell, das über viele Entitäten hinweg trainiert wurde, die erforderliche Variation mit weitaus weniger zu betreibenden Komponenten erfassen.\u003C/p\u003E\n\u003Ch3\u003EGovernance mit dem Modelllebenszyklus verknüpft halten\u003C/h3\u003E\n\u003Cp\u003ETrainingsdaten, Feature-Definitionen und Modellversionen benötigen alle eine nachvollziehbare Datenherkunft, sobald Vorhersagen in die Produktion gehen. Wenn die zugrunde liegenden Daten bereits dort, wo sie sich befinden, der Governance unterliegen, können Teams diese Kontrollen und Beziehungen über den gesamten prädiktiven Workflow hinweg aufrechterhalten. Das Kopieren von Daten in eine andere Umgebung schafft einen weiteren Ort, an dem Zugriffsrichtlinien, Datenherkunft und Definitionen neu erstellt und gepflegt werden müssen. Mit einem klar definierten, messbaren Anwendungsfall zu beginnen, hilft Teams, diesen vollständigen Produktionspfad zu etablieren, bevor sie ihn auf zusätzliche Modelle und Workloads übertragen.\u003C/p\u003E\n\u003Ch3\u003EPrädiktive Modelle sind nicht von Natur aus statisch\u003C/h3\u003E\n\u003Cp\u003EEin Modell, das einmal trainiert und dann nicht mehr angerührt wird, beschreibt irgendwann eine ältere Version der Umgebung. Geplantes Retraining gibt ihm Zugriff auf neuere Beobachtungen. Das Modell-Monitoring verfolgt, ob sich die Performance ändert, während die Erkennung von Data-Drift und Concept-Drift dabei hilft, Verschiebungen bei den Eingaben oder in der Beziehung zwischen diesen Eingaben und dem Ergebnis zu identifizieren. Champion-Challenger-Tests bieten einen weiteren Ansatz: Behalten Sie das aktuelle Produktionsmodell bei, während Sie eine Alternative dagegen evaluieren.\u003C/p\u003E\n\u003Cp\u003EZeitreihen-Workloads erfordern bei der Validierung besondere Aufmerksamkeit. Das zufällige Aufteilen von Datensätzen in Trainings- und Testsets kann das Modell zukünftigen Informationen aussetzen, die zum Zeitpunkt der Vorhersage nicht verfügbar gewesen wären. Die Zeitreihen-Kreuzvalidierung bewahrt die chronologische Reihenfolge, während verzögerte Features es Modellen ermöglichen, frühere Beobachtungen zu nutzen, ohne dass spätere in das Training einfließen. Diese Praktiken sorgen dafür, dass prädiktive Modelle auf die Bedingungen abgestimmt bleiben, die sie vorhersagen sollen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-run-predictive-analytics-on-snowflake":{"id":"title-v2-baf789ce6c","additionalClasses":"anchor-title anchor-title--why-run-predictive-analytics-on-snowflake","type":"heading2","lines":["Warum Predictive Analytics auf Snowflake ausführen?"],":type":"snowflake-site/components/title-v2"},"text_why-run-predictive-analytics-on-snowflake_0":{"id":"text-d4a408476f","text":"\u003Cp\u003EPredictive Analytics funktioniert am besten, wenn Modelle eng mit den von ihnen verwendeten Daten verbunden bleiben. Die Ausführung prädiktiver Workloads in derselben Umgebung mit zentraler Governance wie die Unternehmensdaten reduziert die Notwendigkeit, separate Kopien, Synchronisierungspipelines und Governance-Kontrollen für einen externen ML-Stack zu erstellen. 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Teams können diese Modelle mit SQL trainieren und nutzen, einschließlich der Evaluierung der Modell-Performance und der Anwendung der Modelle auf neue Daten.\u003C/p\u003E\r\n\u003Cp\u003EFür Workloads, die benutzerdefinierte Modelle erfordern, unterstützt \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview\"\u003ESnowflake ML\u003C/a\u003E den breiteren Lebenszyklus für maschinelles Lernen, einschließlich Feature Engineering, Training, Modellmanagement und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/mlops/\"\u003EMLOps.\u003C/a\u003E Mit Snowpark können Teams Python-basierte ML-Workflows für Daten in Snowflake entwickeln, während Snowflake Notebooks eine integrierte Umgebung für Entwicklung und Experimente bietet.\u003C/p\u003E\r\n\u003Cp\u003EDiese Ansätze dienen unterschiedlichen Anpassungsstufen, aber der zugrunde liegende Vorteil ist derselbe: Prädiktive Workloads bleiben über einen größeren Teil des Modelllebenszyklus hinweg an aktuelle Daten mit zentraler Governance angebunden.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_trustworthiness-depends-on-the-surrounding-lifecycle":{"id":"title-v2-d2432462eb","additionalClasses":"anchor-title anchor-title--trustworthiness-depends-on-the-surrounding-lifecycle","type":"heading2","lines":["Vertrauenswürdigkeit hängt vom umgebenden Lebenszyklus ab"],":type":"snowflake-site/components/title-v2"},"text_trustworthiness-depends-on-the-surrounding-lifecycle_0":{"id":"text-acd916c8f2","text":"\u003Cp\u003EPredictive Analytics ist letztlich eine Übung in Generalisierung. Modelle lernen aus historischen Daten und werden dann mit Kund:innen, Transaktionen, Geräten und Betriebsbedingungen konfrontiert, die sie noch nie zuvor gesehen haben.\u003C/p\u003E\n\u003Cp\u003EDeshalb benötigen nützliche prädiktive Systeme mehr als nur ein starkes Trainingsergebnis. Die Daten müssen repräsentativ bleiben, die Evaluierung muss die tatsächliche Vorhersageaufgabe widerspiegeln und das Monitoring muss den Punkt erfassen, an dem frühere Muster nicht mehr gelten. Wenn sich diese Bedingungen ändern, sorgen Retraining und Modellmanagement dafür, dass die Vorhersage auf die Umgebung abgestimmt bleibt, die sie beschreiben soll.\u003C/p\u003E\n\u003Cp\u003EDieselbe Logik gilt unabhängig davon, ob Teams klassische statistische Methoden, prädiktive KI oder eine Kombination aus prädiktiver und generativer KI verwenden. Das Modell liefert eine Schätzung darüber, was als Nächstes kommt; der umgebende Lebenszyklus bestimmt, ob diese Schätzung im Laufe der Zeit vertrauenswürdig bleibt.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_trustworthiness-depends-on-the-surrounding-lifecycle_0":{"id":"text-8085091682","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EWICHTIGSTE ERKENNTNIS\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EPrädiktive Modelle lernen aus historischen Mustern, aber ihre wahre Bewährungsprobe findet mit neuen Daten statt. 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gestellte Fragen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-083270bae2","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EIhre häufigen Fragen zu Predictive Analytics – beantwortet von Snowflake-Expert:innen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-c989a182a6","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Ist Predictive Analytics dasselbe wie maschinelles Lernen?","richText":"\u003Cp\u003ENein. Maschinelles Lernen bezieht sich auf eine Sammlung von Techniken zum Erlernen von Mustern aus Daten, während Predictive Analytics ein analytisches Ziel beschreibt: die Schätzung eines zukünftigen Wertes, einer Wahrscheinlichkeit oder eines Ergebnisses. Maschinelles Lernen unterstützt häufig Predictive Analytics, obwohl auch klassische statistische Techniken prädiktive Modelle hervorbringen können.\u003C/p\u003E"},{"title":"Was ist der Unterschied zwischen KI und Predictive Analytics?","richText":"\u003Cp\u003EKI ist das breitere Feld, das maschinelles Lernen und andere Ansätze umfasst. Predictive Analytics hat einen engeren Zweck: die Nutzung historischer Daten zur Schätzung zukünftiger Ergebnisse. 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