{"id":20863914,"url":"https://github.com/eziodevio/dog-breed-classifier-mlops","last_synced_at":"2026-04-06T08:31:25.172Z","repository":{"id":233995335,"uuid":"788145414","full_name":"EzioDEVio/Dog-Breed-classifier-MLOps","owner":"EzioDEVio","description":"comprehensive guide to setting up a complete MLOps pipeline for a Dog Breed Classifier application using Teachable Machine, TensorFlow, Flask, Docker, and CI/CD practices","archived":false,"fork":false,"pushed_at":"2024-04-17T23:40:29.000Z","size":2631,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-12-26T20:10:02.355Z","etag":null,"topics":["docker","flask-application","machine-learning","mlops","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/EzioDEVio.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-17T21:30:02.000Z","updated_at":"2024-11-07T02:16:33.000Z","dependencies_parsed_at":"2024-11-18T09:20:56.383Z","dependency_job_id":null,"html_url":"https://github.com/EzioDEVio/Dog-Breed-classifier-MLOps","commit_stats":null,"previous_names":["eziodevio/dog-breed-classifier-mlops"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/EzioDEVio/Dog-Breed-classifier-MLOps","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EzioDEVio%2FDog-Breed-classifier-MLOps","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EzioDEVio%2FDog-Breed-classifier-MLOps/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EzioDEVio%2FDog-Breed-classifier-MLOps/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EzioDEVio%2FDog-Breed-classifier-MLOps/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/EzioDEVio","download_url":"https://codeload.github.com/EzioDEVio/Dog-Breed-classifier-MLOps/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EzioDEVio%2FDog-Breed-classifier-MLOps/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31464604,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-05T21:22:52.476Z","status":"online","status_checked_at":"2026-04-06T02:00:07.287Z","response_time":112,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["docker","flask-application","machine-learning","mlops","tensorflow"],"created_at":"2024-11-18T05:34:32.723Z","updated_at":"2026-04-06T08:31:25.154Z","avatar_url":"https://github.com/EzioDEVio.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Build and Push to GHCR](https://github.com/EzioDEVio/Dog-Breed-classifier-MLOps/actions/workflows/MLOps-ci-cd.yml/badge.svg)](https://github.com/EzioDEVio/Dog-Breed-classifier-MLOps/actions/workflows/MLOps-ci-cd.yml)\n\n\n# Dog Breed Classifier - MLOps Tutorial\n\nThis repository provides a comprehensive guide to setting up a complete MLOps pipeline for a Dog Breed Classifier application using Teachable Machine, TensorFlow, Flask, Docker, and CI/CD practices. The goal is to demonstrate how Machine Learning (ML) models are integrated within a continuous integration and continuous deployment (CI/CD) framework.\n\n## Overview\n\nThis tutorial will cover:\n- Training a model with Google's Teachable Machine\n- Setting up a Flask application to serve predictions\n- Containerizing the application with Docker\n- Implementing a CI/CD pipeline using GitHub Actions\n\n## Prerequisites\n\n- Git\n- Python 3.8+\n- Docker\n- A GitHub account\n- Basic familiarity with Flask and Docker\n\n## Step 1: Training the Model\n\n1. Visit [Teachable Machine](https://teachablemachine.withgoogle.com/), create a new image project, upload images of various dog breeds, train the model, and export it as a TensorFlow model.\n2. Download the `model.json` and `weights.bin` files.\n\n## Step 2: Flask Application Setup\n\nCreate a basic Flask application to serve the model. The application will allow users to upload an image and receive the dog breed prediction.\n\n### Project Structure\n\n\nDog-Breed-classifier-MLOps/\n│\n├── app/\n│   ├── static/\n│   │   ├── css/\n│   │   ├── js/\n│   │   └── images/\n│   ├── templates/\n│   ├── __init__.py\n│   ├── views.py\n│   └── predict.py\n│\n├── model/\n│   ├── model.json\n│   ├── group1-shard1of1.bin\n│\n├── tests/\n│   ├── test_app.py\n│\n├── Dockerfile\n├── requirements.txt\n└── README.md\n\n\n### Implementation\n\n- Flask serves a webpage that allows users to upload images.\n- Predictions are made using the TensorFlow model loaded in Flask.\n\n## Step 3: Dockerization\n\nContainerize the Flask application using Docker to ensure it can be deployed consistently across any environment.\n\n```Dockerfile\n# Use a lightweight Python base image\nFROM python:3.9-slim\n\nWORKDIR /app\nCOPY . /app\n\nRUN pip install --no-cache-dir -r requirements.txt\nEXPOSE 5000\n\nCMD [\"gunicorn\", \"--bind\", \"0.0.0.0:5000\", \"app:create_app()\"]\n```\n\n## Step 4: CI/CD Pipeline with GitHub Actions\n\nSet up GitHub Actions to automate testing, building, and deploying the Flask application.\n\n### Workflow\n\n1. **Continuous Integration:**\n   - Run tests.\n   - Build the Docker image.\n\n2. **Continuous Deployment:**\n   - Push the Docker image to a registry.\n   - Deploy the image to a hosting service like Heroku or AWS.\n\n```yaml\nname: CI/CD Pipeline\n\non:\n  push:\n    branches:\n      - main\n  pull_request:\n    branches:\n      - main\n\njobs:\n  build:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/checkout@v2\n      - name: Set up Python\n        uses: actions/setup-python@v2\n        with:\n          python-version: 3.9\n\n      - name: Install dependencies\n        run: |\n          python -m pip install --upgrade pip\n          pip install -r requirements.txt\n\n      - name: Run tests\n        run: |\n          pytest\n\n      - name: Build and push Docker image\n        uses: docker/build-push-action@v2\n        with:\n          context: .\n          push: true\n          tags: user/myapp:latest\n```\n\n## Conclusion\n\nThis tutorial provides a basic framework for building a MLOps pipeline that incorporates machine learning model training, a web application, Docker containerization, and a CI/CD workflow. It aims to guide the integration of machine learning development with production operations to improve the automation and monitoring at all steps of ML system construction.\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feziodevio%2Fdog-breed-classifier-mlops","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feziodevio%2Fdog-breed-classifier-mlops","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feziodevio%2Fdog-breed-classifier-mlops/lists"}