Kubeflow
What it is: Open source ML platform for Kubernetes that makes deploying ML workflows on Kubernetes simple, portable, and scalable.
What It Does Best
ML on Kubernetes natively. Run entire ML lifecycle on Kubernetes without writing complex manifests. Notebooks, pipelines, training, serving - all Kubernetes-native.
Portable pipelines. Define ML workflows once, run anywhere. Cloud-agnostic pipelines work on GCP, AWS, Azure, or on-prem Kubernetes.
Multi-framework support. TensorFlow, PyTorch, XGBoost, scikit-learn - all work seamlessly. Not locked into one framework or vendor.
Key Features
Pipelines: Build and orchestrate ML workflows as Kubernetes resources
Notebooks: Managed Jupyter notebooks on Kubernetes
Training: Distributed training for TensorFlow, PyTorch, MXNet
Serving: Deploy models with KServe for inference
Katib: Hyperparameter tuning and neural architecture search
Pricing
Free: Open source (Apache 2.0 license)
Cloud: Free software, pay only for Kubernetes infrastructure
Managed: Some clouds offer managed Kubeflow (pricing varies)
When to Use It
✅ Already running on Kubernetes infrastructure
✅ Need cloud-portable ML workflows
✅ Want to standardize ML on Kubernetes
✅ Building multi-team ML platform
✅ Need both training and serving at scale
When NOT to Use It
❌ Not using Kubernetes (steep learning curve)
❌ Small team or simple workflows (overkill)
❌ Prefer managed ML platforms (SageMaker, Vertex AI easier)
❌ No DevOps/infrastructure team to maintain it
❌ Just getting started with ML (too complex)
Common Use Cases
ML platform: Build internal ML infrastructure for teams
Multi-cloud ML: Run same workflows across clouds
Production pipelines: Automate model training and deployment
Research to production: Seamless transition from notebooks to serving
Distributed training: Scale training across Kubernetes cluster
Kubeflow vs Alternatives
vs SageMaker: Kubeflow cloud-agnostic, SageMaker AWS-only but easier
vs MLflow: Kubeflow full platform, MLflow lighter tracking/serving
vs ClearML: ClearML easier setup, Kubeflow more Kubernetes-native
Unique Strengths
Kubernetes-native: True cloud-native ML platform
Cloud portable: Works on any Kubernetes, any cloud
Full ML lifecycle: Development, training, serving in one platform
Open ecosystem: Large community and extensible architecture
Bottom line: Best ML platform if you're committed to Kubernetes. Perfect for multi-cloud organizations or teams that need cloud portability. Complex setup but powerful once running. Only choose if you have Kubernetes expertise and need enterprise-scale ML.