Ray + Kubernetes: The Production AI Stack Explained скачать в хорошем качестве

Ray + Kubernetes: The Production AI Stack Explained

*Why do 92% of ML models never reach production?* It's not a code problem—it's a platform engineering problem. In today's episode of Platform Engineering Playbook, we tackle the massive infrastructure gap that's keeping AI initiatives stuck in notebooks while your data science teams wonder why their brilliant models never see the light of day. *What You'll Learn:* ✅ The real reasons ML models fail to reach production (hint: it's your infrastructure) ✅ How to architect production-ready AI infrastructure using Ray on Kubernetes ✅ Practical strategies for platform engineers supporting data science teams ✅ Enterprise GitOps scaling from single clusters to fleet management *Episode Breakdown:* 0:00 Cold Open - The 92% problem 2:15 Industry News Roundup 8:30 Deep Dive: From Notebooks to Production 15:45 Architecture Analysis: Ray on Kubernetes *Today's Platform Engineering News:* • Datadog's new audit-ready compliance reporting • Amazon Bedrock transforming HR talent acquisition • The hidden cost of burning out your on-call engineers • Enterprise GitOps fleet management strategies Whether you're struggling with ML infrastructure or just want to stay ahead of platform engineering trends, this episode gives you actionable insights you can implement today. *Sources & References:* From notebooks to nodes: Architecting production-ready AI infrastructure: https://thenewstack.io/production-ai-... Generate audit-ready vulnerability and compliance reports with Datadog Sheets: https://www.datadoghq.com/blog/audit-... AI meets HR: Transforming talent acquisition with Amazon Bedrock: https://aws.amazon.com/blogs/machine-... Is your on-call rotation quietly burning out top talent?: https://thenewstack.io/sustainable-on... How to scale GitOps in the enterprise: From single cluster to fleet management: https://platformengineering.org/blog/... #PlatformEngineering #DevOps #CloudNative #Kubernetes