Networking Optimizations for Multi-Node Deep ...

For more than a decade, AI has advanced by training ever-larger models on ever-larger datasets. But as high-quality training data becomes harder to find and pretraining grows increasingly expensive, researchers are looking for new ways to keep foundation models improving. In this ...  Show more

In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the science behind smell, from the hundreds of olfactory receptors in the human nose to ...  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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