Key Notes
- Kubeflow - Deployment of ML workflow on Kubernetes
- Different companies different platforms, Leverage Kubeflow as a deployment platform
- ML Aspects of Deployment
- CI / CD requirements, Iterate and push code to production
- Complexities in ML, Diverse tools Notebook / IDE, Notebook to pipeline, CUJ - Critical user journey
- Kubeflow pipelines. Data versioning, snapshots, Tools - Kale, Arrikto (Data Management)
- Deploying Challenges, Composable pipeline, Single steps for different hardware
Kubeflow, MiniKF. Choose Deployment Name in Cloud Console, Kubeflow-kale
Code - Link
Happy Learning!!!
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