This project shows how to serve an ONNX-optimized image classification model as a web service with FastAPI, Docker, and Kubernetes.

Production-ready Claude Code configuration for backend/full-stack developers. 9 rules, 8 commands, 5 agents, 13 skills, hooks, and MCP servers for Java/Spring Boot, Python/FastAPI, JS/React, PostgreSQL, MongoDB, Redis, Kafka, Docker, K8s, and AI/ML.

The project is a machine learning (ML) deployment tutorial and MLOps practice. It deploys a video classification model trained on Kinetics-400 as a simple FastAPI app to Kubernetes with minikube.

A cloud-native iris ML inference service built with FastAPI, containerized using Docker, locally tested on Kubernetes (Minikube), and designed for scalable AWS deployment.

This application demonstrates deploying machine learning models using FastAPI, Docker, and Kubernetes. It includes background task processing with Celery and Redis, and provides endpoints for making predictions and retrieving results. The application uses the Breast Cancer Wisconsin (Diagnostic) dataset for model training and evaluation

Full-stack League of Legends replay analyzer — FastAPI backend, React canvas visualizer, ML performance scoring, and K8s deployment

Enterprise AI Gateway built with FastAPI, LiteLLM, and LangFuse. Features ML-based routing across 25+ models (OpenAI, Anthropic, Gemini, Grok, DeepSeek), semantic caching with FAISS, multi-tenancy with Redis-backed rate limiting, streaming SSE, conversational memory, prompt templates, function calling, and Terraform/K8s deployment.

AI-powered phishing detection system using multi-model ML (NLP/BERT, CNN/ResNet-50, GNN, URL analyzer) with ensemble decision engine. Real-time threat analysis <100ms via FastAPI. Browser extension + admin dashboard. Detects email/SMS/web phishing with >95% accuracy. Docker/K8s deployment. Continuous learning from threat feeds & user feedback.

FastAPI + MLflow + Docker + K8s ML pipeline

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

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Train/log, deploy, and monitor an NLP model like an engineer.

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This project demonstrates how to train an XGBoost model with MLflow tracking and deploy it as a FastAPI service on Kubernetes.

Playing with building an ML API using fastapi, docker and k8s

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simple AIV app (C#) with microservices-using FastAPI Backend and ML and DL models ,K8S , Docker and more

Diabetes Prediction MLOps Project (FastAPI + Docker + K8s) – Learn to build and deploy an ML model predicting diabetes based on health metrics (Pregnancies, Glucose, Blood Pressure, BMI, Age) using a Random Forest Classifier on the Pima Indians dataset. Covers model training, FastAPI API, Docker, and Kubernetes deployment.

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.

ObservaGuard is an AI-powered DevOps platform that detects Kubernetes configuration drift and secret leaks. It combines FastAPI, React, Go, and an IsolationForest ML model, with Docker, Helm, and Prometheus integration, offering a full microservices stack deployable locally or on K8s.

Full-stack ML platform with React (TypeScript) frontend and FastAPI backend. Supports end-to-end workflows: data wrangling, manipulation, feature engineering, selection, scaling, modeling, hyperparameter tuning, and deployment. Features RESTful APIs, educational UI, Docker/K8s support, and robust error handling for seamless experimentation and depl