Building Enterprise Solutions in the AI-Native Era
summary
Red Hat AI claims an open source, hybrid-cloud platform that provisions sandboxes in minutes and runs models “wherever the data resides,” addressing cost and model-selection pain points that leave >95% of enterprises struggling despite ~2.2M models on Hugging Face. Demos show a registry with 545 validated models, an MCP protocol to connect LMs to CRMs and PDF tools, and a Quarkus Java framework path so Java teams can build agentic apps without hiring Python-only ML engineers; customers (airline, government, Hitachi) report faster validation, automated reviews, and improved call‑center recommendations.
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chapters (12)
- 0:00AI-native enterprise solutions
- 2:07Why AI projects fail
- 4:36AI adoption success stories
- 8:52AI deployment challenges
- 12:46Open source AI models
- 15:21AI model inference runtimes
- 17:28Validating AI models for enterprise
- 20:20LM Studio for AI orchestration
- 21:26Red Hat AI platform
- 24:18Red Hat AI registry demo
- 28:11AI apps with Java and Quarkus
- 33:50Summary and future
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