Building with open source generative AI models and frameworks

• Titel: Building with open source generative AI models and frameworks
• Mitwirkende: Chang, Susan Shu [MitwirkendeR]
• Mitwirkende: Spiteri, James [MitwirkendeR]
• Mitwirkende: Linkov, Denys [MitwirkendeR]
• Mitwirkende: Regmi, Avin [MitwirkendeR]
• Mitwirkende: Gu, Mandy [MitwirkendeR]
• Mitwirkende: Königstein, Nicole [MitwirkendeR]
• Mitwirkende: Werra, Leandro van [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., 48 min.))
• Illustrationen: sound, color.
• Gesamttitel/Reihe: AI Superstream
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Intelligence artificielle ; Applications industrielles
• Sach-SW: Logiciels libres ; Applications industrielles
• K10plus-PPN: 1899109153
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4568072727
• Lokale URL Inst.: Zum Volltext

Abstract

The landscape of open source technologies for building AI applications has expanded rapidly since the advent of LLMs and other frontier AI models. But there are ongoing discussions about what constitutes open source AI, and current technologies come with a range of limitations and benefits. Proprietary models may be optimal in some cases, but concerns about cost, privacy, and security for production-grade applications may require a closer look at open source options. Join us to explore the features and capabilities of the latest open source AI models, frameworks, and platforms, from Mistral and LangChain to Hugging Face. You'll learn how to leverage these and other open source AI tools to build more robust and cost-effective AI applications, right from the industry experts already putting them to work in the field.