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.

Frontend: Streamlit (Python-based web application framework) Backend: Python AI/ML: OpenAI's language models via the OpenAI API Database: Neo4j graph database Orchestrator: Langchain Containerization: Docker Cloud Platform: Microsoft Azure (Azure Container Instances) Version Control: Git

The Machine Learning framework and backend for zeta.ml. Deployed using Docker and Kubernetees. The Official Project from Team Zeta for HACKMIT2020.

End-to-end FastAPI backend showcasing async programming, dependency injection, JWT authentication, PostgreSQL integration with SQLModel, Redis caching, background tasks (Celery), and Dockerized deployment. Designed as a clean, extensible backend template for ML or data applications.

Dog Breed Recognition is a web app that uses deep learning to classify dog breeds from images. Built with React for the frontend, Flask for the backend, and Keras for the ML model, this project is containerized with Docker. The model was trained using the Stanford Dogs Dataset and fine-tuned for optimal accuracy.

Dual-backend health risk prediction system using Spring Boot API gateway + FastAPI ML service with trained scikit-learn model and Docker support.

🛰️ Backend Python FastAPI offline-ready pour anticiper les pannes réseau militaires par l’IA, avec génération de données synthétiques, modèles ML optionnels et déploiement Docker optimisé.

A front end dashboard for a credit scoring app, deployed on Heroku inside a Docker container. The backend is a REST API server using a custom ML model.

Real-time telecom data streaming pipeline with Kafka, PostgreSQL/Supabase/AWS & Streamlit dashboard | Production-ready ETL with Docker.🎯 Perfect for: Data Engineers, ML Engineers, Backend Developers 📊 Use Cases: Telecom Analytics, Real-time Monitoring, Streaming ETL.Features live CDR (Call Detail Record) processing, interactive dashboards.

ml backend for label studio UAV predictions

Dockerized FastAPI ML backend for predicting insurance premium categories using scikit-learn

Production-grade FastAPI backend for ML-based car price prediction with caching, monitoring, and Docker deployment

A polyglot microservice application that automates pull‑request code reviews using ML‑driven linting and static analysis. Includes a Go gRPC backend, Python FastAPI ML service, React + TypeScript dashboard, Docker‑compose setup, and GitHub Actions CI.

📦 Diet Recommendation System: Personalized meal suggestions powered by ML and GPT, with a user-friendly Streamlit interface and FastAPI backend. Docker-ready for instant deployment.

ML Diabetes Prediction API is a production-ready machine learning backend built with FastAPI and Docker, deploying an AdaBoost classification model to predict diabetes risk based on medical parameters.

Backend API for interpreting hand gestures to control a maze game. Serves a trained ML model via FastAPI, supports image-based inference, and includes Dockerized deployment with Prometheus and Grafana monitoring.

A full-stack backend system to manage student data, calculate analytics, and predict risk levels using **MongoDB**, **FastAPI**, and **Python**. Includes **ML integration**, aggregation pipelines, TTL indexes, and deployment-ready Docker setup.

Platform for finding similar products using image similarity. Built with Dockerized microservices: React frontend, FastAPI backend, BentoML ML service with CLIP model, Minio object storage, and Oracle database. Upload images to find visually similar products, with automated setup and orchestration via Docker Compose.

AI-powered job application assistant that analyzes job descriptions, parses resumes, calculates semantic match scores, and generates personalized cover letters using NLP and LLMs. Full-stack ML application with FastAPI backend, Streamlit frontend, 85% test coverage, and Docker deployment.

CloudGuardian AI is a multi‑cloud platform for monitoring, predictive maintenance, and automated remediation across AWS/Azure/GCP. FastAPI backend, React frontend, ML risk scoring, and remediation playbooks. Dockerized for local demos; production‑ready with OAuth, secrets, and audit hooks.

Autonomous ML pipeline powered by LangGraph — upload a dataset, describe your objective, and watch it profile, engineer features, visualize, train models, and self-evaluate through an LLM Critic loop. Zero notebooks, zero manual tuning. FastAPI backend, Next.js Neural Observatory dashboard, Docker-native, AWS-ready.

This is a complete, enterprise-grade full-stack application built for agile teams. It features a React frontend, a Node.js/GraphQL backend, a Python ML service for story point estimations, and a full DevOps pipeline using Docker, Kubernetes, and Terraform for cloud deployment.

End-to-end ML anomaly detection platform for search performance monitoring, built with Apache Kafka, PySpark streaming, IsolationForest inference, real-time FastAPI backend. Full containerized stack (Docker, Kubernetes) demonstrates production-ready MLOps: data pipeline orchestration, model versioning with MLflow, and multi-tier caching aggregation

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A lightweight backend for serving ML models with FastAPI & Docker.

Production-ready FastAPI ML backend for insurance risk & premium prediction (Dockerized)

Label Studio + MinIO + SAM3 ML Backend — Docker Compose stack with Cloudflare Tunnel

Backend: Spring Boot ML Model: Python (Scikit-learn) Database: MySQL Security: JWT Containerization: Docker

FastAPI-based credit risk prediction backend with ML inference, database integration, and Docker support.