Bridging the gap between an impressive AI demo and a scalable production environment is the single biggest hurdle for enterprise leaders today. Instead of relying on costly Frontier Models for every task, successful production strategies require right-sizing your tools.
In this episode of Technically Speaking, Chris Wright and Red Hatter Cat Weeks discuss the strategy behind moving AI out of the lab and into the real world. They explore why the future of enterprise AI isn’t a monolith, but a diverse fleet of Large Language Models (LLMs), Small Language Models (SLMs), and agentic AI. Learn how to experiment early, right-size your models, and build a strategy that scales securely.
Topic timestamps:
00:00 – Introduction: The problem with using a sledgehammer to hang a picture frame
01:45 – The Human Element: Overcoming fear and upskilling teams
03:55 – ""A stupid process automated is an automated stupid process""
05:10 – The Data Challenge: Ownership and stewardship
06:50 – Security: Dynamic identity and the ""Agent Mesh""
09:20 – RAG vs. Fine-Tuning: When to use which?
11:30 – The Cost & Performance benefits of SLMs
Learn More:
Read the top considerations for building a production-ready AI/ML environment e-book: https://www.redhat.com/en/resources/building-production-ready-ai-ml-environment-e-book
What is an AI platform? https://www.redhat.com/en/topics/ai/what-is-an-ai-platform
AI infrastructure explained: https://www.redhat.com/en/topics/ai/ai-infrastructure-explained
Follow us:
Chris Wright: https://www.linkedin.com/in/chris-wright-b733851/
Cat Weeks: linkedin.com/in/catherineweeks
What is Technically Speaking? Technically Speaking taps into emerging technology trends with insights from leading experts across the globe and Red Hat CTO Chris Wright. The series blends deep-dive discussions, tech updates, and creative short-form content, solidifying Red Hat’s role as a pioneer in technology innovation and open source thought leadership.
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