Challenges and takeaways of managing AI workloads on cloud
summary
Model deployment routinely takes longer than model development, and the solution is specific: adopt a data catalog to centralize lineage and ownership, run Kubeflow notebooks as containers so experiments execute in-cluster, use the Kubeflow training operator to scale distributed training, front third‑party models with an AI Gateway to centralize auth, prompts and caching, and monitor for data/prediction/concept drift with Prometheus and Grafana. An internal developer platform plus conversational DevX glues these pieces into self‑service workflows that actually reduce handoffs and failures.
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chapters (12)
- 0:00Introduction and Session Overview
- 0:22The Problem with AI Model Deployment
- 1:12Understanding the AI Workflow
- 1:59Challenges in Data Management
- 3:52Bridging the Development and Production Gap
- 4:10Tools for Cloud Native AI
- 4:57Data Governance and Culture
- 6:35Kubeflow for Seamless AI Deployment
- 9:25AI Gateways and Model Monitoring
- 13:19Leveraging Platform Engineering
- 14:15The Future of AI in Development
- 16:13Conclusion and Key Takeaways
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