MLOps engineering at scale

• Verfasst von: Osipov, Carl [VerfasserIn]
• Titel: MLOps engineering at scale
• Verf.angabe: Carl Osipov
• Ausgabe: [First edition].
• Verlagsort: [Shelter Island, New York]
• Verlag: Manning Publications
• E-Jahr: 2022
• Jahr: [2022]
• Umfang: 1 online resource (1 sound file (7 hr., 8 min.))
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed July 2, 2024)
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Apprentissage automatique
• Sach-SW: Audiobooks
• Sach-SW: Livres audio
• K10plus-PPN: 1899110410
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4568083672
• Lokale URL Inst.: Zum Volltext

Abstract

MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production.