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Kubeflow

by CNCF

Open-source CNCF ML platform built on Kubernetes, enabling APAC data science and ML engineering teams to run end-to-end ML pipelines — from data preparation through distributed training, hyperparameter tuning, and model serving — on shared Kubernetes GPU infrastructure.

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AIMenta verdict
Recommended
5/5

"Kubeflow is the open-source ML platform on Kubernetes for APAC data science teams — end-to-end ML pipelines covering data preparation, training, tuning, and serving on shared GPU infrastructure. Best for APAC teams standardising ML workflows on existing Kubernetes clusters."

Features
7
Use cases
4
Watch outs
4
What it does

Key features

  • Kubeflow Pipelines — Python SDK for defining reproducible APAC ML workflows as versioned, schedulable DAGs
  • Training Operator — distributed training CRDs for TensorFlow, PyTorch, and XGBoost on APAC Kubernetes GPU clusters
  • KServe — Kubernetes-native model serving with autoscaling and canary deployment for APAC production ML inference
  • Notebooks — Kubernetes-based Jupyter notebook servers with GPU access for APAC data science development
  • Katib — hyperparameter tuning using Bayesian optimisation, grid search, and NAS for APAC model optimisation
  • Feature Store integration — compatible with Feast and Tecton for APAC ML feature serving
  • Multi-tenancy — namespace-based isolation and resource quotas for shared APAC GPU cluster governance
When to reach for it

Best for

  • APAC ML engineering teams standardising ML workflows on existing Kubernetes infrastructure with GPU node pools
  • AI platform teams building shared ML infrastructure that multiple APAC data science teams can use with namespace isolation
  • Engineering organisations wanting reproducible, version-controlled APAC ML pipelines integrated with Kubernetes GitOps workflows
  • APAC data science teams that need distributed training capabilities (multi-GPU, multi-node) beyond single-machine training
Don't get burned

Limitations to know

  • ! Steep learning curve — Kubeflow requires Kubernetes expertise, ML workflow design skills, and familiarity with the Kubeflow component ecosystem; APAC teams new to both Kubernetes and MLOps face significant ramp-up
  • ! Installation and upgrade complexity — self-managed Kubeflow on APAC Kubernetes requires expertise in Kustomize, Istio, and Kubernetes operators; upgrades between Kubeflow versions have historically been challenging
  • ! Overhead for small ML teams — Kubeflow's infrastructure investment (Istio service mesh, MinIO for artifact storage, MySQL for metadata) is significant for APAC teams with 2-3 data scientists; MLflow or Weights & Biases may deliver more value at lower operational cost
  • ! Managed offering gaps — unlike MLflow (Databricks) or SageMaker (AWS), there is no single definitive managed Kubeflow offering; APAC teams must self-manage or use Kubeflow-based products from cloud vendors with varying support quality
Context

About Kubeflow

Kubeflow is an open-source CNCF ML platform built on Kubernetes that enables APAC data science and machine learning engineering teams to develop, train, deploy, and monitor machine learning models using Kubernetes as the execution substrate — providing a suite of Kubernetes-native components that cover each stage of the ML lifecycle while sharing APAC GPU infrastructure across multiple data science teams and projects.

Kubeflow Pipelines — where ML workflows are defined as Python-based DAGs using the Kubeflow Pipelines SDK (or Argo Workflows underneath), with each pipeline step executing as a Kubernetes container — enables APAC ML engineering teams to version-control, schedule, and rerun reproducible ML pipelines for data preprocessing, model training, evaluation, and deployment, with full execution history and artifact tracking stored in the Kubeflow metadata database.

Kubeflow Training Operator — where custom CRDs (TFJob, PyTorchJob, MXJob, XGBoostJob, MPIJob) enable APAC data science teams to submit distributed training jobs that Kubernetes schedules across multiple GPU nodes — enables ML engineers to run multi-GPU distributed training on existing APAC Kubernetes GPU clusters without managing distributed training infrastructure manually.

KFServing / KServe — Kubeflow's model serving component that provides APAC ML engineering teams with a Kubernetes-native inference server supporting TensorFlow, PyTorch, XGBoost, scikit-learn, and custom models through a standardised prediction API — enables APAC teams to deploy trained models to production Kubernetes with autoscaling, canary deployments, and monitoring without building custom model serving infrastructure.

Kubeflow's multi-tenancy model — where Kubernetes namespaces provide isolation between APAC data science teams, with profile-based resource quotas controlling GPU allocation per team — enables APAC AI platform teams to run a shared Kubeflow cluster serving multiple data science teams, enforcing resource limits and access control while maximising GPU utilisation across the organisation.

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