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MLOps-Grundlagen: Bereitstellen und Verwalten von Machine-Learning-Modellen in der Produktion
Paperback
Publisher: Independently Published
Published: Aug 31 2025
Pages: 288
Weight: 0.85
Height: 0.60 Width: 6.00 Depth: 9.00
Language: German
Bereitstellen und Verwalten von Machine-Learning-Modellen in der Produktion
Von Modellen zu Mehrwert - Machine Learning erfolgreich in Produktion bringen.
Mit MLOps-Grundlagen erhalten Sie einen praxisnahen Leitfaden, wie Sie Machine-Learning-Modelle zuverlässig entwickeln, bereitstellen und betreiben. Statt Modelle nur im Labor zu bauen, lernen Sie, wie diese in realen Umgebungen skalierbar, reproduzierbar und sicher funktionieren.
Einführung in MLOps: Konzepte, Vorteile und Best Practices
Unterschiede zwischen ML-Entwicklung und ML-Betrieb
Aufbau einer ML-Pipeline: Daten, Training, Validierung, Deployment
Modellversionierung und Nachvollziehbarkeit (MLflow, DVC)
Automatisierung mit CI/CD für Machine Learning
Containerisierung und Deployment mit Docker und Kubernetes
Monitoring von ML-Modellen in der Produktion (Drift, Performance, Logging)
Retraining und Continuous Learning
Sicherheit, Governance und Skalierung im Produktivbetrieb
Praxisprojekt: Von einem ML-Modell bis zur produktionsreifen API
Data Scientists, die ihre Modelle produktiv einsetzen möchten
ML Engineers, die stabile Pipelines bauen wollen
Softwareentwickler, die KI in bestehende Systeme integrieren möchten
IT-Teams, die skalierbare ML-Lösungen betreiben wollen
Klare, praxisnahe Erklärungen ohne unnötige Theorie
Mit Tools, Frameworks und Best Practices aus der Industrie
Schritt für Schritt von der Modellentwicklung bis zum Deployment
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