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Elastic machine learning
Relevante Antworten mit KI und ML finden Die Elasticsearch-Plattform bietet native Integrationen mit leistungsstarken Machine-Learning- und KI-Lösungen, damit Sie die Nutzer mit Ihren Anwendungen begeistern und produktiver arbeiten können.
Elastic Machine Learning liefert Antworten und Einblicke für alle Nutzer. Nutzen Sie die Vorteile von Machine Learning im Handumdrehen mit fachspezifischen Anwendungsfällen, die direkt in Elasticsearch integriert sind. Mit den Lösungen für Observability , Suche und Sicherheit können DevOps-Spezialisten, SREs und Sicherheitsanalysten sofort loslegen. Sie benötigen keinerlei Vorkenntnisse im Bereich Machine Learning.
Ihre Teams können die Anomalieerkennung und Ursachenanalysen automatisieren, um die mittlere Reparaturdauer zu reduzieren. Weitere integrierte Funktionen wie natürliche Sprachverarbeitung und Vektorsuche unterstützen Ihre Teams zusätzlich bei der Implementierung benutzerfreundlicher Sucherlebnisse.
Elastic Machine Learning bietet die folgenden Möglichkeiten:
Identifizieren ungewöhnlich langer Antwortzeiten direkt in der APM-Dienstkarte Erkennung ungewöhnlicher Verhaltensweisen und proaktiver Umgang mit Sicherheitsbedrohungen Anpassen der Anomalieerkennung für beliebige Datentypen mit benutzerdefinierten, assistentenbasierten Workflows Optimieren von Sucherlebnissen durch Anreicherung der ingestierten Daten mit Vorhersage
Automatisieren von Warnungen und Identifizieren von Ursachen mit Observability Beschleunigen Sie die Erkennung und Behebung von Problemen mit automatisierter Anomalieerkennung, Korrelationen und anderen AIOps-Funktionen , die direkt in Elastic Observability integriert sind. DevOps- und SRE-Teams können ungewöhnlich lange Antwortzeiten direkt in der APM-Dienstkarte identifizieren. Sie können Machine Learning anwenden, ohne Modelle konfigurieren zu müssen.
Threat Hunting mit Machine-Learning-Unterstützung Die Bedrohungserkennung in Elastic Security nutzt Machine-Learning-Funktionen. Sie können die mittlere Reparaturdauer (Mean Time to Resolution, MTTR) reduzieren, indem Sie ungewöhnliche Aktivitäten in der SIEM-App automatisch identifizieren. Im Fall von schwer zu identifizierenden Bedrohungen helfen beaufsichtigte Modelle, verdächtige und gutartige Aktivitäten zu unterscheiden, etwa für improvisierte Angriffe oder Domain-Generation-Algorithmen .
Sucherlebnisse auf höchstem Niveau Mit der Elasticsearch Relevance Engine™ (ESRE) können Sie semantische Suchfunktionen mit überragender Relevanz sofort (ohne fachspezifische Anpassung) einsetzen, eigene LLMs integrieren, Hybrid-Sucherlebnisse implementieren und externe oder eigene Transformationsmodelle nutzen.
Umsetzbare Einblicke innerhalb von Minuten mit Elasticsearch Machine Learning Elastic Machine Learning bietet die folgenden Möglichkeiten für Ihre Daten:
Machine Learning nativ in einer skalierbaren und leistungsstarken Plattform integrieren Unbeaufsichtigte Lernfunktionen und vorkonfigurierte Modelle anwenden, um Observability- und Sicherheitsprobleme zu identifizieren, ohne ein eigenes KI-Modell trainieren zu müssen Umsetzbare Analytics nutzen, um Bedrohungen und Anomalien proaktiv zu erkennen, Probleme schneller zu beheben, Verhaltenstrends von Kunden zu erkennen und Ihr digitales Erlebnis zu verbessernSie brauchen kein Data-Science-Team oder eine spezielle Systemarchitektur, um Elastic Machine Learning nutzen zu können. Mit unseren Machine-Learning-Funktionen können Sie im Handumdrehen loslegen! Sie müssen keine Daten in Frameworks von Drittanbietern verschieben, um Modelle zu trainieren.
Für Anwendungsfälle, die benutzerdefinierte Modelle und Leistungsoptimierungen erfordern, können Sie mit unseren Tools die entsprechenden Parameter anpassen und optimierte Modelle aus dem PyTorch-Framework importieren.
Ingestieren, verstehen und erstellen Sie Modelle mit Ihren eigenen Daten. Mit den vorkonfigurierten Integrationen können Sie mühelos Daten ingestieren und mit anderen Datenquellen verbinden. Sobald Ihre Daten in Elasticsearch verfügbar sind, können Sie innerhalb von Minuten Visualisierungen erstellen und Einblicke gewinnen.
Mit dem offenen und allgemeinen Elastic-Modell, dem Elastic Common Schema (ECS), können Sie beliebige Daten flexibel erfassen, speichern und visualisieren. Dazu gehören auch Metriken, Logs, Traces, Inhalte und Ereignisse aus Ihren Anwendungen und Ihrer Infrastruktur. Wählen Sie zunächst eine Ingestionsmethode aus, um loszulegen. Neben Optionen wie Elastic Agent, Web-Crawler , Daten-Connectoren und APIs bieten wir auch native Integrationen mit allen wichtigen Cloud-Anbietern. Sobald Sie Ihre Daten in Elastic ingestiert haben, können Sie integrierte Tools wie den Data Visualizer nutzen, um Felder in Ihren Daten zu identifizieren, die sich für Machine-Learning-Anwendungsfälle eignen.
Sie haben noch keine Erfahrung mit Machine Learning? Nutzen Sie die vorkonfigurierten Modelle für optimale Observability und Sicherheit. Falls diese Modelle für Ihre Daten nicht ausreichen, können Sie mit den in die Tools integrierten Assistenten in nur wenigen Schritten eine benutzerdefinierte Anomalieerkennung konfigurieren und überwachte Lernfunktionen trainieren.
Machine-Learning-Assistenten
Zielgenaue, vorkonfigurierte Anomalie- und Ausreißererkennung Unbeaufsichtigtes Machine Learning mit Elastic hilft Ihnen, Muster in Ihren Daten zu finden. Mit der Zeitreihenmodellierung können Sie Anomalien in einzelnen oder mehreren Zeitreihen oder Populationsdaten erkennen und Trends anhand von historischen Daten vorhersagen.
Außerdem können Sie Nachrichten gruppieren, um Anomalien in Logs zu erkennen, und Ursachen aufdecken, indem Sie Anomalieauslöser oder korrelierte Felder mit Abweichungen von Baselines überprüfen.
Beaufsichtigtes Machine Learning ohne zusätzlichen Aufwand Trainieren Sie Klassifizierungs- oder Regressionsmodelle mit Data-Frame-Analytics in Elastic, um Ihre Daten zu kategorisieren und Vorhersagen zu treffen. Mit beaufsichtigten Modellen können Sie Problemursachen leichter erkennen und intelligente Entscheidungen in Ihren Anwendungen treffen.
Verwenden Sie kontinuierliche Indextransformationen, um einen Anwendungslogindex in eine nutzerzentrische Aktivitätsansicht umzuwandeln, und erstellen Sie Betrugserkennungsmodelle mithilfe von Klassifizierung. Anschließend können Sie Ihre Modelle beim Ingestieren auf die eingehenden Daten anwenden, ohne Elastic jemals verlassen zu müssen.
Vektorsuche und moderne natürliche Sprachverarbeitung Mit der semantischen Vektorsuche finden Ihre Nutzer, wonach sie suchen, und zwar ohne Einschränkungen durch Schlüsselwörter. Die Nutzer können Textdaten, Bilder und andere unstrukturierte Daten durchsuchen.
Mit dem Elastic Learned Sparse Encoder können Sie eine semantische Suche implementieren und erhalten überragende domänenübergreifende Relevanz im vorkonfigurierten Zustand. Auf diese Weise können Sie intuitive digitale Erlebnisse und relevante Ergebnisse anbieten. Beispiele:
Ähnlichkeitssuche für Produkte im E-Commerce-Bereich, um Produktalternativen anzuzeigen Jobempfehlungen und Online-Dating – Empfehlungen anhand von Profilkompatibilität und Einschränkung der Suche nach Geolocation Patentsuche – Suche nach Patenten mit ähnlichen Textbeschreibungen Als Einstiegshilfe können Sie mit Elastic vorab trainierte BERT-ähnliche PyTorch-Modelle aus Hubs wie etwa Huggungface.co oder das CLIP-Modell von OpenAI importieren. Erfahren Sie mehr über die Implementierung einer Bildähnlichkeitssuche mit Elastic.