Standardized Serverless ML Inference Platform on Kubernetes

Machine Learning Pipelines for Kubeflow

Elyra extends JupyterLab with an AI centric approach.

Distributed ML Training and Fine-Tuning on Kubernetes

Automated Machine Learning on Kubernetes

Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

Kubeflow’s superfood for Data Scientists

Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo

👩‍🔬 Train and Serve TensorFlow Models at Scale with Kubernetes and Kubeflow on Azure

Helmut Hoffer von Ankershoffen experimenting with arm64 based NVIDIA Jetson (Nano and AGX Xavier) edge devices running Kubernetes (K8s) for machine learning (ML) including Jupyter Notebooks, TensorFlow Training and TensorFlow Serving using CUDA for smart IoT.

A declarative KubeFlow Management Tool

Charmed Kubeflow

This repo covers Kubeflow Environment with LABs: Kubeflow GUI, Jupyter Notebooks on pods, Kubeflow Pipelines, Experiments, KALE, KATIB (AutoML: Hyperparameter Tuning), KFServe (Model Serving), Training Operators (Distributed Training), Projects, etc.

Code for tutorials and examples

🎱 A demonstration of existing machine learning toolkits on Kubernetes

Orchestrate Spark Jobs from Kubeflow Pipelines and poll for the status.

Kedro Plugin to support running workflows on Kubeflow Pipelines

☁️ Export Ploomber pipelines to Kubernetes (Argo), Airflow, AWS Batch, SLURM, and Kubeflow.

This repository aims to develop a step-by-step tutorial on how to build a Kubeflow Pipeline from scratch in your local machine.

👩‍🔬[Experimental] Easily train and serve ML models on Kubernetes, directly from your python code.