Tag Archives: Artificial Intelligence

DeepThink AI and Kubernetes

We springboard from DeepThinking AI and have a robust conversation about what impact DeepThink is having on the industry. We also discuss where we see things going into the dilemma of people building AI infrastructure and working to do that quickly, robustly and with strong governance. This is necessary to ensure that they can quickly update and manage that AI infrastructure that they’re spending so much money to build, and this leads into a broader conversation about virtualization, containers and open shift.

Recorded Jan 30, 2025

Transcript: otter.ai/u/79JxdYOiXUoSS44pYP…?utm_source=copy_url

Reference: www.perplexity.ai/search/provide-a…TQ6LJG_X0SlB5g#8

Why is adding LLM into an App so hard?

We talk about current events, the acquisition of data stacks and the closing of the HashiCorp acquisition by IBM. Later, we dive into the productivity of AI and what’s going on – are companies really getting the benefits that they expect from AI chat bot integrations and what the challenges are?

We touch base on a little bit of something more infrastructure focused, where I give a preview of work I’ve been doing on separating Kubernetes virtualization from Kubernetes development use cases, which is something that we will be talking about more in the future.

References:
www.windowscentral.com/software-apps…ind-a-paywall
www.ibm.com/new/announcements/i…ise-ai-applications
www.youtube.com/watch?v=Ioc3r70HNLM
www.linkedin.com/posts/dhinchclif…9498138624-jR2R/
20250227

Symbolic AI

We discuss Symbolic AI via LLMs for advanced reasoning in manufacturing and real-time analytics. Key points included leveraging symbolic representations and algebraic equations, utilizing knowledge graphs to improve model accuracy, and exploring agentic AI frameworks with specialized agents working together using swarm intelligence principles to tackle complex problems like anomaly detection and process optimization. The group also discussed the challenges of building trust in AI systems and the importance of capturing and storing questions and answers to build a knowledge base.

Generative DevOps

NOTE: This is Rob’s Gluecon topic on 5/24. Save $300 if you register with speaker300 at www.gluecon.com

We dive into the question of whether or not generative AI can be used to productively change DevOps automation and the control of infrastructure.

We’ve discussed the closed loop side of using AI to manage infrastructure in the past, but this episode we really dive into the idea of creating automation and using generative AI.

Transcript: otter.ai/u/VtnznHgydT3_6QSJpk…?utm_source=copy_url
Image: www.pexels.com/photo/tossing-fri…ying-pan-6937457/

Machine Learning in Operations

Today’s episode is about how to trust machine learning in operations. This is a really serious issue because the attraction of machine learning is strong, but does not translate into operations.

Why doesn’t it translate? Because operations is a closed loop process where we constantly get feedback and have to adapt and adjust. That makes it difficult to train models and hope that they work. This discussion gets into why that’s the case and what we can do about it.

Then we explore scenarios for machine learning and AI in operations.

Transcript: otter.ai/u/UBjf5IVnKvebTfQgW1xlneZdjOU
Photo: www.pexels.com/photo/high-angle-…of-robot-2599244/