Networking Optimizations for Multi-Node Deep ...

As reasoning models consume more tokens and AI systems become more expensive to run, understanding what those tokens actually buy is becoming increasingly important. In this episode, Stanford professor and Big Spin co-founder Chris Potts joins us to discuss AI tokenomics and his ...  Show more

In this episode, Justin Johnson, co-founder of World Labs, joins us to discuss world models and the emerging field of spatial AI. We explore why many researchers see capabilities beyond language as an important frontier for AI, and what it means to build models that can understan ...  Show more

On today's Kubernetes Unpacked we speak with Evis Drenova about whether AI has a role to play in the Kubernetes ecosystem. We discuss everything from using chat-based generative AI tools to interact with Kubernetes to data models and data sets on Kubernetes to Machine Learning wi ...  Show more

Where there are containers, there is networking. Today we dig into the networking that underlies Kubernetes, the open source orchestration platform for container-based applications. Our guest Karim El Jamali takes us through the essential concepts: Nodes, pods, clusters, CNIs, vi ...  Show more

In this episode, Michael catches up with Natan Yellin, CEO of Robusta.dev. Michael and Ned chat about how logging and troubleshooting works in Kubernetes today. They discuss three stages of troubleshooting evolution - manual playbooks, automatic playbooks, and the third stage, wh ...  Show more

This is the last episode of the series "Embedded ML" and I made it for the bravest :) I speak about machine learning compiler optimization to a much greater detail.

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