Workshop: Empowering Collaboration: AI Developer Experience - Your Bridge from Mod...
summary
Shows an end‑to‑end GenAI pipeline that fine‑tunes Stable Diffusion 1.5 with DreamBooth on images from an S3 bucket, exports to ONNX, runs a KubeFlow/CouPipelines job (~5–10 minutes), and serves the model via KServe/Model Mesh on Kubernetes; a Flask app scaffolded with Backstage and deployed with Argo CD calls the gRPC/HTTP endpoint to generate images (example: “Red Hat Teddy” successfully produced). Tools: Jupyter Hub, KServe, Backstage.
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chapters (13)
- 0:00AI developer experience intro
- 3:52AI/ML lifecycle explained
- 11:29AI models fail in production
- 18:32Building AI platforms open source
- 23:28MLOps importance
- 27:20Demo tools overview
- 30:19Jupyter Hub workflow
- 39:51Fine-tuning with KubeFlow
- 45:53Serving models with KServe
- 50:31Backstage workflow
- 55:30Connecting scientists developers
- 56:11Boston University use case
- 56:55Resources sandbox
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