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.

Train and Deploy Machine Learning Models on Kubernetes using Amazon EKS

Dockerized basic tweet classifier app. Hate speech and offensive language detection model using various Machine Learning and NLP techniques. Also, Hate Speech Detection for tweets with k8s Cluster

Machine Learning to detect ELK log anomalies in real time using Actions, Docker, K8S, Terraform

Operationalizing a working machine learning microservice using k8s

Machine learning pipelines running using Kubeflow over K8s

Operationalize a machine learning API using docker and k8s

A machine learning application deployed using docker and k8s

Developing K8s platform using AWS EFS and kubeflow for machine learning

End-to-end machine learning inference services using FastAPI, Docker and K8s

A test repository for running Machine (Deep) Learning jobs using dokcer and k8s.

Scraping k8s cluster and use the data to build a machine learning model to predict resource usage

This Git repository contains the code used in the Medium article A Reactive Machine Learning system on top K8s

WIP This is a k8s cluster that collects stock prices and sentiment data, processes it, and runs experiments on stock trading and predictions using machine learning

Machine Learning Model Deployment using FastAPI, Docker, and Kubernetes. Exposes ML models via REST API for real-time inference, containerized for portability, and deployable on local/VPS/K8s with CI/CD support. Scalable, production-ready, and easy to integrate.

This project demonstrates how to deploy a machine learning model for image classification using a FastAPI REST API, containerized with Docker, and orchestrated via Kubernetes (K8s). It showcases a full end-to-end MLOps workflow — from model export to scalable deployment — designed to bridge the gap between research and production.

This project, ML-K8s-Fastapi, is a machine learning API for Iris flower classification. It's built using Scikit-learn for the ML model and FastAPI to create the API. The application is Dockerized, making it ready for deployment, particularly to Kubernetes. It provides a /predict endpoint to classify Iris flowers based on their sepal and petal