In this episode, David Aronchick, CEO and Co-founder of Expanso discusses his experiences and insights from working with Kubernetes since its early days at Google. David shares his journey from working on Kubernetes to co-founding Kubeflow and his latest project, Bacalhau, which focuses on combining compute and data management in distributed systems. Highlighting the challenges of data processing and privacy, particularly in edge computing and regulated environments, David emphasizes cost-saving benefits and the importance of local data processing. Throughout, privacy and regulatory concerns are underscored along with solutions for efficient and secure data handling.

00:00 Introduction and Welcome

00:23 Early Days of Kubernetes

01:05 Kubernetes Community and Evolution

02:23 AI, ML, and KubeFlow

03:40 Current Work and Data Challenges

08:20 Privacy and Security Concerns

14:21 Real-World Applications and Benefits

20:42 Conclusion

In this episode, we sit down with Victor Nagy of GitLab to discuss his role and GitLab's initiatives. Victor details the transition from using a custom solution to integrating Flux for smoother application deployment. Victor also talks about GitLab's commitment to the open source community, contributions to Flux, and becoming a potential maintainer. We also touch on what makes developer tools great, developer experience, and developments in AI and security, highlighting the rapid pace of innovation in these fields.

00:00 Introduction and Guest Introduction

00:36 Key Open Source Projects: Flux and GitLab

01:17 Choosing Flux

03:42 Community Contributions and Future Plans

05:35 Deployment and Product Management

12:31 GitLab's Comprehensive Platform and Differentiators

18:38 Security and AI

19:43 Conclusion and Final Thoughts

In this episode, Marlow Warnicke, lead for the Slinky project, and Tim Wickberg, CTO of SchedMD, join us to discuss their work integrating HPC scheduler Slurm with Kubernetes. They provide background on Slurm's origins, its open source nature, and its evolution into Slinky to address Kubernetes's limitations in scheduling AI and HPC workloads. The discussion touches on the unique challenges in the MLOps space, the need for fine-grained resource control, and their collaborative efforts with various communities to enhance Kubernetes's efficiency. They also share the roadmap for Slinky and avenues for community collaboration and contribution.

00:00 Introduction and Guest Introductions

00:39 Overview of Slurm and Its Evolution

01:44 The Fusion of Slurm and Kubernetes: Slinky

04:14 Challenges in Kubernetes Scheduling

09:07 Unique Challenges in MLOps

12:58 Community Collaboration and Future Plans

16:41 Getting Involved and Final Thoughts