Building Foundational ML Platforms with Kubernetes and Kubeflow with Ali Rodell - #595
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Navigating Kubernetes: Challenges and Strategies
This chapter explores the complexities of managing distributed computing in Kubernetes and Kubeflow, addressing issues such as operational challenges, self-DDoS risks, and the fragility of critical components like etcd. It emphasizes the trade-offs faced when balancing stateless Kubernetes designs with stateful machine learning tasks and suggests strategies for workload management. Furthermore, the chapter highlights the integration of source control in data science workflows, advocating for a harmonious blend of traditional software practices with the innovative nature of data science.
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