Engineering performant and trustworthy AI solutions

• Titel: Engineering performant and trustworthy AI solutions
• Titelzusatz: ensuring AI product quality and reliability
• Mitwirkende: Mohanna, Ammar [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 (2 hr., 27 min.))
• Illustrationen: sound, color.
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed October 28, 2024)
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Intelligence artificielle ; Qualité ; Contrôle
• Sach-SW: Apprentissage automatique ; Évaluation
• 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: 1910507660
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4629551190
• Lokale URL Inst.: Zum Volltext

Abstract

This course focuses on the unique challenges and methodologies involved in testing and validating AI and machine learning models. It provides a comprehensive understanding of the paradigms and practices essential for assuring the quality and reliability of AI-powered products. The course covers the technical, practical, and business perspectives of AI QA, offering participants the tools and knowledge needed to enhance their AI development processes. As AI technologies become increasingly integral to various industries, ensuring their reliability and performance is crucial. Quality assurance in AI is not just about verifying accuracy but also about addressing issues like data quality, algorithmic bias, and model explainability. For AI developers, engineers, and QA professionals, mastering these aspects is vital to delivering robust, market-ready AI solutions that meet business objectives and user expectations. This course addresses the specific challenges of testing AI systems, including handling non-deterministic outputs, managing data biases, and ensuring continuous learning and adaptation. It provides practical solutions for integrating QA processes into the AI development lifecycle, helping professionals mitigate risks, enhance model performance, and maintain ongoing reliability. By understanding and applying effective QA strategies, participants can overcome common obstacles in AI projects, ultimately leading to more successful deployments.