Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes

Hopsworks - Data-Intensive AI platform with a Feature Store

🪐 1-click Kubeflow using ArgoCD

Carbon Limiting Auto Tuning for Kubernetes

AWS SageMaker, SeldonCore, KServe, Kubeflow & MLflow, VectorDB

The Machine Learning Zoomcamp teaches foundational and advanced ML concepts using tools like NumPy, Pandas, Scikit-Learn, TensorFlow, XGBoost, Flask, Docker, AWS, Kubernetes, and KServe. It covers regression, classification, evaluation metrics, neural networks, deployment strategies, and end-to-end projects to bridge theory and practice.

My repo for the Machine Learning Engineering bootcamp 2022 by DataTalks.Club

End-to-end MLOps architecture built for polyp segmentation — featuring distributed Ray training, MLflow experiment tracking, and automated CI/CD with Kubeflow Pipelines and KServe (Triton) deployment on Google Kubernetes Engine.

This repository demonstrates how to deploy, scale, and monitor machine learning models on Kubernetes using KServe and Kubeflow Lite components.

Deploying machine learning model using 10+ different deployment tools

Client/Server system to perform distributed inference on high load systems.

Hands-on labs on deploying machine learning models with tf-serving and KServe

Production inference server for Reactant-compiled models, serving KServe V2 over gRPC

prokube.ai examples - Notebooks, Pipelines, Experiment Tracking, Model tuning, and more

A demo to accompany our blogpost "Scalable Machine Learning with Kafka Streams and KServe"

A science inference cluster — every model, from protein folding to LLMs, behind one OpenAI/Anthropic-compatible endpoint and a single key. Built to run beside HPC so agentic AI researchers can drive simulations and model inference together. Self-deploying on RKE2 with KServe/Knative + HAMi GPU.

Observability for vLLM on KServe — dashboards, PromQL, SLOs, and a closed-loop load generator

In this video, we’ll walk you through building a powerful machine learning model using Kubeflow and deploying it seamlessly to KServe with InferenceService!

Production-pattern Red Hat OpenShift AI 3.4.0 platform with bare-metal ESXi, GPU passthrough, KServe RawDeployment, DeepSeek R1 inference at 12–17 tok/s

Everything to get industrial kubeflow applications running in production