Introducing the Kubeflow SDK: A Pythonic API to Run AI Workloads at Scale

Unified SDK Concept Introducing Kubeflow SDK Role in the Kubeflow Ecosystem Key Features Unified Python Interface Trainer Client Optimizer Client Local Execution Mode Local Process Backend: Fastest Iteration Container Backend: Production-Like Environment Kubernetes Backend: Production Scale What’s Next? Get Involved Unified SDK Concept Introducing Kubeflow SDK Role in the Kubeflow Ecosystem Role in the Kubeflow Ecosystem Key Features Unified Python Interface Trainer Client Optimizer Client Local Execution Mode Local Process Backend: Fastest Iteration Container Backend: Production-Like Environment Kubernetes Backend: Production Scale Unified Python Interface Trainer Client Optimizer Client Local Execution Mode Local Process Backend: Fastest Iteration Container Backend: Production-Like Environment Kubernetes Backend: Production Scale Local Process Backend: Fastest Iteration Container Backend: Production-Like Environment Kubernetes Backend: Production Scale What’s Next? Get Involved ⚡ We want your feedback! Help shape the future of Kubeflow SDK by taking our quick survey. Scaling AI workloads shouldn’t require deep expertise in distributed systems and container orchestration. Whether you are prototyping on local hardware or deploying to a production Kubernetes cluster, you need a unified API that abstracts infrastructure complexity while preserving flexibility. That’s exactly what the Kubeflow Python SDK delivers. As an AI Practitioner, you’ve probably experienced this frustrating journey: you start by prototyping locally, training your model on your laptop. When you need more compute power, you have to rewrite everything for distributed training. You containerize your code, rebuild images for every small change, write Kubernetes YAMLs, wrestle with kubectl, and juggle multiple SDKs — one for training, another for hyperparameter tuning, and yet another for pipelines. Each step demands different tools, APIs, and mental models. All this complexity slows down productivity, drains focus, and ultimately holds back AI innovation. What if there was a better way? The Kubeflow community started the Kubeflow SDK & ML Experience Working Group (WG) in order to address these challenges. You can find more information about this WG on our YouTube playlist. The SDK sits on top of the Kubeflow ecosystem as a unified interface layer.