Designing machine learning systems in the cloud

• Titel: Designing machine learning systems in the cloud
• Mitwirkende: Gallatin, Kyle [MitwirkendeR]
• Institutionen: O'Reilly (Firm), [Verlag]
• Ausgabe: [First edition].
• Verlagsort: [Sebastopol, California]
• Verlag: O'Reilly Media, Inc.
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: 1 online resource (1 video file (1 hr., 16 min.))
• Illustrationen: sound, color.
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed August 19, 2024)
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Apprentissage automatique
• Sach-SW: Instructional films
• Sach-SW: Nonfiction films
• Sach-SW: Internet videos
• Sach-SW: Films de formation
• Sach-SW: Films autres que de fiction
• Sach-SW: Vidéos sur Internet
• K10plus-PPN: 1903893429
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4585390006
• Lokale URL Inst.: Zum Volltext

Abstract

This course is an introduction to designing and implementing production machine learning (ML) systems that will give students the skills necessary to architect and deploy ML solutions to the cloud. This course is for anyone with at least 2 years of programming experience and familiarity with ML looking to deliver real business value with ML in real, enterprise software systems. Using Python, Docker, Kubernetes, and Google Cloud, students will learn the practical skills necessary to build end-to-end ML systems, from engineering training data, features and data pipelines to deploying and monitoring ML models for real-time inference in production.