The Open-Source AI Enterprise (el. knyga) (skaityta knyga) | knygos.lt

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A strategic guide to building enterprises around open-source AI tools and frameworks

The Open-Source AI Enterprise: Build It, Run It, Grow It shows executives and technical decisionmakers how to leverage open-source AI tools and frameworks to drive business value. Written by Marius Bogoevici, an enterprise technology leader with broad experience in open source and AI, currently with HPE and previously at Red Hat, Pivotal, and SpringSource, the book addresses how organizations can evaluate open-weight models, adopt open-source AI platforms, and build strategies that keep pace with a rapidly evolving ecosystem.

The book goes beyond implementation guidance to examine how AI fundamentally changes the way organizations should approach open-source technologies. Topics include selecting among proprietary and open-source models, navigating AI-generated content including open-source code, and engaging effectively with open-source communities. Bogoevici draws on direct experience advising enterprises to outline how organizations can continuously monitor emerging tools and adapt to new breakthroughs.

Readers will also find:

• Strategies for evaluating and adopting open-weight models within enterprise AI architectures, deployment workflows, and production environments
• Guidance on building adaptive organizational structures that continuously monitor, evaluate, and integrate emerging open-source AI tools and platforms
• Frameworks for contributing back to open-source communities to create a self-sustaining cycle of innovation, collaboration, and lasting value
• Analysis of how AI-generated content, including open-source code, reshapes enterprise technology strategies and decision-making processes
• Approaches for balancing proprietary and open-source AI solutions to maximize return on investment across the entire organization

Written for executives, CTOs, and technical decision-makers evaluating open-source AI strategies, this book provides a structured approach to selecting open-source tools and models, building adaptive organizations, and engaging with open-source communities to sustain long-term competitive advantage in AI adoption.