£69.17

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£69 today · cheaper than every other day in the last 3 months

LLMs entwickeln und deployen: Von der Idee bis zur produktiven Anwendung

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£69.17 £65.71 £67.09 £68.48 £69.86 £71.25 £72.63 28 July 2026 10 August 2026 23 August 2026 05 September 2026 19 September 2026
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Price distribution over 54 days • 1 price levels

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54 days 0 14 27 41 54 £69 Days at Price

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Most common price: £69 (54 days, 100.0%)

Price range: £69 - £69

Price levels: 1 different prices over 54 days

Description

Meistern Sie LLMs mit „LLMs entwickeln und deployen“ Möchten Sie Large Language Models (LLMs) erfolgreich entwickeln und deployen? „LLMs entwickeln und deployen“ von Erik Gieske (Copyright 2025) ist Ihr Schlüssel dazu. Dieses 394-seitige Werk führt Sie von den Grundlagen bis zur produktiven Anwendung – ideal für erfahrene Softwareentwickler, Data Scientists und ML-Engineers. Warum dieses Buch? Umfassende Einführung: Lernen Sie neuronale Netze, Deep Learning und Transformer-Architekturen (Kapitel 1, 3). Implementieren Sie Forward- und Backpropagation in PyTorch. Praxisorientiert: Von Datenerfassung mit Apache Spark (Kapitel 6) über GPU-Training (Kapitel 8) bis zu Echtzeit-Inferenz (Kapitel 14) – mit Beispielen wie Finanztextanalyse. Industrie-Fokus: Entwickeln Sie skalierbare Datenpipelines, optimieren Sie Hyperparameter und setzen MLOps für reproduzierbare Experimente ein (Kapitel 19). DSGVO-Compliance und Bias-Erkennung (Kapitel 17, 18) sind zentral. Rollenaufteilung: Verstehen Sie die Zusammenarbeit von Data Engineers, ML-Engineers, DevOps- und Security-Experten (Seiten 5–16). Übungen: Praktische Aufgaben wie Backpropagation in NumPy oder DSGVO-Checklisten fördern Ihre Kompetenz. Für wen? Perfekt für Fachleute mit Python- und ML-Kenntnissen, die LLMs in Start-ups oder Großunternehmen einsetzen wollen: Data Scientists: Optimieren Sie Datenpipelines. ML-Engineers: Implementieren und fine-tunen Sie Modelle. DevOps-Spezialisten: Nutzen Sie Docker und Kubernetes. Security-Experten: Sichern Sie Datenschutz und Ethik. Inhalte Theorie: Neuronale Netze, Aktivierungsfunktionen, Optimierungsalgorithmen (Kapitel 1). Praxis: Transformer, Spark-Pipelines, GPU-Training (Kapitel 4, 6, 8). Deployment: Docker, Kubernetes, Echtzeit-Inferenz (Kapitel 13, 14). Ethik: Bias-Optimierung, DSGVO (Kapitel 17, 18). Zukunft: Low-Resource-Training, neue Architekturen (Kapitel 11, 21). Jetzt kaufen! Die Nachfrage nach LLM-Expertise wächst. Dieses Buch spart Zeit, erfüllt regulatorische Anforderungen und boostet Ihre Karriere.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
30 August 2025
Listed Since
31 August 2025

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