Kubeflow

Kubeflow — Technical Use Cases

Kubeflow is purpose-built for teams running AI/ML and HPC workloads on Kubernetes. Common technical use cases include:

• Distributed model training — Run multi-node, multi-GPU training jobs using PyTorch, JAX, DeepSpeed, Megatron, or MLX via the Kubeflow Training Operator and Kubernetes CRDs
• Large language model (LLM) fine-tuning — Leverage HuggingFace and DeepSpeed/Megatron integrations for efficient fine-tuning of foundation models on self-hosted infrastructure
• Automated hyperparameter tuning — Use Katib (AutoML) to run parallel hyperparameter search and neural architecture search as native Kubernetes jobs
• ML pipeline orchestration — Define and execute DAG-based ML workflows (data prep → training → evaluation → serving) using Kubeflow Pipelines
• Big data preprocessing — Integrate Apache Spark and XGBoost for large-scale feature engineering and gradient-boosted model training
• Model versioning & lineage — Track model artifacts, versions, and provenance through the Model Registry subproject
• Platform engineering for AI — Deploy a composable, modular ML platform on any Kubernetes cluster (on-prem, hybrid, or cloud-agnostic) with GitOps-friendly declarative configuration