Going pro

• Titel: Going pro
• Titelzusatz: cloud computing
• Mitwirkende: Gift, Noah [PräsentatorIn]
• Institutionen: Pragmatic AI Solutions (Firm), [Verlag]
• Verf.angabe: Noah Gift
• Ausgabe: [First edition].
• Verlagsort: [Place of publication not identified]
• Verlag: Pragmatic AI Solutions
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: 1 online resource (1 video file (20 hr., 1 min.))
• Illustrationen: sound, color.
• Gesamttitel/Reihe: Pragmatic AI labs course
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed May 14, 2024)
• Datenträger: Online-Ressource
• Sprache: eng
• 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: 1890486515
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4533732410
• Lokale URL Inst.: Zum Volltext

Abstract

This comprehensive course teaches you to leverage the power of cloud computing to develop production-ready machine learning solutions. Starting with the fundamentals of cloud storage, security, and infrastructure as code, you will gain a solid foundation in cloud computing concepts. The course then dives into serverless machine learning, teaching you to build and deploy ML models using popular cloud AutoML platforms like Google AutoML, Amazon SageMaker, Microsoft Azure ML, and CreateML. Through hands-on labs, you'll learn to train custom models with just a few lines of configuration, without needing deep ML expertise. Next, the course explores serverless technologies like AWS Lambda, Google Cloud Functions, and Azure Functions to cost-effectively operationalize your ML models at scale. You'll learn to expose your AutoML models as serverless microservices, automatically scaling to handle any load. The course also covers serverless data processing and ETL to build complete ML pipelines. Finally, you'll learn to apply DevOps best practices to your serverless ML applications using continuous integration and delivery (CI/CD), infrastructure as code, monitoring, and containerization technologies like Docker and Kubernetes. By the end, you'll have the skills to build highly scalable end-to-end machine learning solutions leveraging the latest cloud AutoML and serverless technologies.