Why Kubeflow?
Kubeflow is the Kubernetes-native ML platform I use for orchestrating complex ML workflows at scale. It provides the building blocks for production ML systems on Kubernetes.
Key Components I Use
Kubeflow Pipelines
- DAG-based workflow orchestration
- Reusable pipeline components
- Experiment tracking integration
- Scheduled and triggered runs
Notebooks
- Jupyter notebooks on Kubernetes
- GPU/CPU resource allocation
- Persistent volumes for data
- Collaboration and sharing
Training Operators
- Distributed training support
- TensorFlow, PyTorch operators
- Resource management
- Fault tolerance
Serving
- Model deployment and serving
- Auto-scaling capabilities
- A/B testing support
- Canary deployments
My Experience
At CML Insights, I've built comprehensive ML pipelines using Kubeflow:
Data Pipelines
- Ingestion from multiple sources
- Transformation and feature engineering
- Validation and quality checks
- Feature store integration
Training Pipelines
- Hyperparameter optimization
- Distributed training workflows
- Experiment tracking with MLflow
- Model validation and testing
Deployment Pipelines
- Automated model deployment
- Smoke testing in staging
- Progressive rollout to production
- Monitoring integration
Architecture
Kubeflow on EKS with:
- S3 for pipeline artifacts
- PostgreSQL for metadata
- MLflow for model registry
- ArgoCD for deployment
Benefits
- Kubernetes-native workflows
- Resource efficiency
- Reproducible experiments
- Version-controlled pipelines
- Team collaboration