Kubernetes AI Landscape

A comprehensive collection of AI/ML tools, frameworks, and resources in the Kubernetes ecosystem for building, deploying, and managing machine learning workloads at scale.

Table of Contents

• Overview
• MLOps Platforms
• Model Serving & Inference
• Workflow Orchestration
• Training & Experimentation
• Data Processing & Pipelines
• Monitoring & Observability
• GPU & Resource Management
• Development & Notebooks
• ? MCP Servers for Kubernetes
• Security & Compliance
• Commercial & Managed Solutions
• Getting Started
• Contributing
• Community

Overview

Kubernetes has become the de facto standard for orchestrating AI/ML workloads, providing scalability, portability, and robust resource management for machine learning operations. This repository catalogs the essential tools and frameworks that power the Kubernetes AI ecosystem.

Why Kubernetes for AI/ML?

• Scalability: Dynamic scaling of ML workloads based on demand
• Portability: Deploy anywhere Kubernetes runs (cloud, on-premise, edge)
• Resource Management: Efficient GPU/CPU allocation and optimization
• Containerization: Consistent environments from development to production
• Automation: GitOps and CI/CD integration for ML pipelines

MLOps Platforms

Comprehensive platforms for end-to-end machine learning lifecycle management.

Tool	Description	Category	License	GitHub Stars	Key Features
Kubeflow	Complete ML platform for Kubernetes with pipelines, notebooks, and model serving	End-to-End MLOps	Apache 2.0	14k+	Pipelines, Notebooks, Katib, KServe, Multi-framework support
MLflow	Open source platform for ML lifecycle management	Experiment Tracking	Apache 2.0	18k+	Tracking, Projects, Models, Registry, Deployment
ZenML	Extensible open-source MLOps framework for reproducible pipelines	MLOps Framework	Apache 2.0	4k+	Pipeline orchestration, Model deployment, Stack management
Metaflow	Human-friendly library for data science projects	Data Science Platform	Apache 2.0	8k+	Versioning, Scaling, Deployment, Human-centric design

Model Serving & Inference

Tools for deploying and serving ML models in production environments.

Tool	Description	Category	License	GitHub Stars	Supported Frameworks
KServe	Kubernetes-native serverless ML inference platform	Model Serving	Apache 2.0	5.5k+	TensorFlow, PyTorch, XGBoost, SKLearn, ONNX, Hugging Face
Seldon Core	ML deployment platform for Kubernetes with advanced features	Model Serving	BSL 1.1	4k+	SKLearn, XGBoost, SparkML, Custom models
Ray Serve	Scalable model serving library with Python-first approach	Model Serving	Apache 2.0	33k+	Any Python framework, PyTorch, TensorFlow, SKLearn
TensorFlow Serving	Production ML model serving system for TensorFlow	Model Serving	Apache 2.0	6k+	TensorFlow, TensorFlow Lite
TorchServe	PyTorch model serving framework	Model Serving	Apache 2.0	4k+	PyTorch, TorchScript, ONNX
Triton Inference Server	NVIDIA's inference serving software	Model Serving	BSD 3-Clause	8k+	TensorFlow, PyTorch, ONNX, TensorRT, Custom backends
BentoML	Framework for building ML services	Model Serving	Apache 2.0	7k+	All Python ML frameworks

Workflow Orchestration

Tools for managing and orchestrating ML pipelines and workflows.

Tool	Description	Category	License	GitHub Stars	Key Capabilities
Argo Workflows	Container-native workflow engine for Kubernetes	Workflow Orchestration	Apache 2.0	15k+	DAG workflows, Parallel execution, Artifact management
Apache Airflow	Platform for workflow orchestration and scheduling	Workflow Orchestration	Apache 2.0	36k+	Python DAGs, Rich UI, Extensive integrations
Tekton	Cloud-native CI/CD pipeline framework	CI/CD Pipelines	Apache 2.0	8k+	Kubernetes-native, Reusable tasks, GitOps
Kubeflow Pipelines	ML workflow orchestration platform	ML Pipelines	Apache 2.0	Part of Kubeflow	ML-specific, Component reuse, Experiment tracking
Prefect	Modern workflow orchestration platform	Workflow Management	Apache 2.0	16k+	Python-native, Dynamic workflows, Error handling

Training & Experimentation

Frameworks and tools for distributed training and hyperparameter optimization.

Tool	Description	Category	License	GitHub Stars	Training Support
Katib	Kubernetes-native hyperparameter tuning	Hyperparameter Tuning	Apache 2.0	Part of Kubeflow	AutoML, NAS, Multi-objective optimization
Ray	Distributed computing framework for ML	Distributed Training	Apache 2.0	33k+	Distributed training, Hyperparameter tuning, Reinforcement learning
Horovod	Distributed deep learning training framework	Distributed Training	Apache 2.0	14k+	TensorFlow, PyTorch, MXNet, Multi-GPU/Multi-node
PyTorch Lightning	High-level interface for PyTorch	Training Framework	Apache 2.0	28k+	Scalable training, Multi-GPU, TPU support
TensorFlow Extended (TFX)	End-to-end ML platform for TensorFlow	ML Platform	Apache 2.0	2k+	Data validation, Transform, Training, Serving

Data Processing & Pipelines

Tools for data ingestion, processing, and pipeline management.

Tool	Description	Category	License	GitHub Stars	Data Support
Apache Spark	Unified analytics engine for big data processing	Data Processing	Apache 2.0	39k+	Batch, Streaming, ML, SQL, Graph processing
Dask	Parallel computing library for Python	Data Processing	BSD 3-Clause	12k+	Pandas, NumPy, Scikit-learn scaling
Flyte	Cloud-native workflow automation platform	Data Orchestration	Apache 2.0	5k+	Type-safe pipelines, Versioning, Multi-cloud
Pachyderm	Data versioning and pipelines for ML	Data Versioning	Apache 2.0	6k+	Git-like data versioning, Pipeline automation
DVC	Data version control for ML projects	Data Versioning	Apache 2.0	13k+	Git integration, Experiment tracking, Model management

Monitoring & Observability

Tools for monitoring ML models and infrastructure performance.

Tool	Description	Category	License	GitHub Stars	Monitoring Features
Prometheus	Monitoring and alerting toolkit	Infrastructure Monitoring	Apache 2.0	55k+	Metrics collection, Alerting, Time-series DB
Grafana	Observability and monitoring platform	Visualization	AGPL 3.0	62k+	Dashboards, Alerting, Multi-datasource
TensorBoard	Visualization toolkit for ML experiments	ML Monitoring	Apache 2.0	Part of TF	Metrics visualization, Model graphs, Profiling
MLRun	Open MLOps platform for managing ML lifecycle	MLOps Monitoring	Apache 2.0	1.4k+	Experiment tracking, Model monitoring, Feature store
Evidently	ML model monitoring and data drift detection	Model Monitoring	Apache 2.0	5k+	Data drift, Model performance, Interactive reports

GPU & Resource Management

Specialized tools for GPU scheduling and resource optimization.

Tool	Description	Category	License	GitHub Stars	GPU Features
NVIDIA GPU Operator	GPU resource management for Kubernetes	GPU Management	Apache 2.0	1.8k+	Automated GPU setup, Driver management, Monitoring
Volcano	Batch system for high-performance workloads	Batch Scheduling	Apache 2.0	4k+	Gang scheduling, GPU affinity, Queue management
Yunikorn	Resource scheduler for big data and ML workloads	Resource Scheduling	Apache 2.0	400+	Multi-tenant, Resource quotas, Preemption
NVIDIA Run:ai	GPU orchestration platform	GPU Orchestration	Commercial	N/A	Dynamic GPU allocation, Workload management, Multi-tenancy

Development & Notebooks

Interactive development environments and notebook platforms.

Tool	Description	Category	License	GitHub Stars	IDE Support
JupyterHub	Multi-user notebook server	Notebook Platform	BSD 3-Clause	8k+	Jupyter notebooks, Multi-user, Spawners
Kubeflow Notebooks	Jupyter notebooks in Kubernetes	ML Notebooks	Apache 2.0	Part of Kubeflow	Pre-configured images, Volume support, RBAC
Code Server	VS Code in the browser	Cloud IDE	MIT	67k+	VS Code, Remote development, Extensions
Kale	Convert Jupyter notebooks to Kubeflow pipelines	Notebook Automation	Apache 2.0	600+	Notebook to pipeline, Auto-annotation, Katib integration

? MCP Servers for Kubernetes

Model Context Protocol (MCP) servers enable AI assistants to interact with Kubernetes clusters through standardized interfaces. ? View Complete MCP Servers Guide

Popular Kubernetes MCP Servers

Tool	Description	Language	Key Features
kubernetes-mcp-server	Native Kubernetes/OpenShift MCP server	Go	Cross-platform binaries, Helm support, OpenShift support
mcp-k8s-go	Lightweight extensible Kubernetes MCP server	Go	Pod logs, Events, Namespaces, Extensible architecture
k8s-multicluster-mcp	Multi-cluster Kubernetes operations	Python	Multi-cluster support, Context switching

Quick Start with MCP

# Install the recommended MCP server
npx kubernetes-mcp-server@latest

# Add to Claude Desktop config
{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["kubernetes-mcp-server@latest"]
    }
  }
}

Use Cases:

• ?? Natural language cluster management
• ? Automated troubleshooting with AI
• ? Resource discovery and analysis
• ? Security auditing assistance

Security & Compliance

Tools and frameworks for securing ML workloads and ensuring compliance.

Tool	Description	Category	License	GitHub Stars	Security Features
Istio	Service mesh for microservices	Service Mesh	Apache 2.0	35k+	mTLS, Traffic policies, Security policies
Open Policy Agent (OPA)	Policy engine for cloud-native environments	Policy Management	Apache 2.0	9k+	Policy as code, Admission control, RBAC
Falco	Runtime security monitoring	Runtime Security	Apache 2.0	7k+	Anomaly detection, Rule engine, Kubernetes-aware
Cosign	Container signing and verification	Supply Chain Security	Apache 2.0	4k+	Image signing, SBOM, Attestations

Commercial & Managed Solutions

Enterprise and cloud-managed platforms for Kubernetes AI/ML.

Platform	Provider	Description	Key Features
Google Vertex AI	Google Cloud	Managed ML platform	AutoML, Custom training, Model serving, Pipelines
Amazon SageMaker	AWS	Complete ML service	Notebooks, Training, Hosting, Pipelines
Azure Machine Learning	Microsoft	Cloud ML service	Designer, AutoML, MLOps, Responsible AI
Databricks	Databricks	Unified analytics platform	Collaborative notebooks, MLflow, Delta Lake
H2O.ai	H2O.ai	AI/ML platform	AutoML, Model interpretability, MLOps

Getting Started

?? Prerequisites

• Kubernetes cluster (v1.20+)
• kubectl configured
• Basic understanding of containers and Kubernetes
• Python/R for ML development

? Quick Start Options

Option 1: Complete MLOps with Kubeflow

# Install kfctl
wget https://github.com/kubeflow/kfctl/releases/download/v1.2.0/kfctl_v1.2.0_linux.tar.gz
tar -xvf kfctl_v1.2.0_linux.tar.gz
sudo mv kfctl /usr/local/bin/

# Deploy Kubeflow
export KF_NAME=my-kubeflow
export BASE_DIR=${HOME}/kubeflow
export KF_DIR=${BASE_DIR}/${KF_NAME}
export CONFIG_URI="https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_k8s_istio.v1.2.0.yaml"

mkdir -p ${KF_DIR}
cd ${KF_DIR}
kfctl apply -V -f ${CONFIG_URI}

Option 2: Model Serving with KServe

kubectl apply -f https://github.com/kserve/kserve/releases/download/v0.8.0/kserve.yaml

Option 3: Workflow Orchestration with Argo

kubectl create namespace argo
kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/download/v3.4.4/install.yaml

Option 4: AI-Native Cluster Management with MCP

npx kubernetes-mcp-server@latest

? Useful Resources

Documentation & Guides

• CNCF AI/ML Working Group
• Kubernetes AI/ML Best Practices
• MLOps Principles
• Model Context Protocol Documentation
• Quick Start Guide

Community & Events

• KubeCon + CloudNativeCon
• MLOps World
• Kubeflow Community

? Contributing

We welcome contributions! Please read our Contributing Guide for details.

Quick Contribution Steps

1. Fork the repository
2. Create a feature branch (git checkout -b feature/add-new-tool)
3. Add your changes in the appropriate category table
4. Commit your changes (git commit -am 'Add new ML tool')
5. Push to the branch (git push origin feature/add-new-tool)
6. Create a Pull Request

? Community

Join our community to discuss Kubernetes AI/ML topics:

• Slack: Collabnix Community
• Twitter: @collabnix
• Blog: Collabnix.com
• YouTube: Collabnix Channel

? License

This project is licensed under the MIT License - see the LICENSE file for details.

? Acknowledgments

• Thanks to all the open-source contributors in the Kubernetes and AI/ML communities
• Special recognition to CNCF projects that power cloud-native AI/ML
• Inspired by the Cloud Native Landscape project
• MCP servers community for advancing AI-infrastructure integration

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Maintained by: Collabnix Community Last Updated: May 2025

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