{"id":20130540,"url":"https://github.com/md-emon-hasan/mlflow-basic","last_synced_at":"2026-04-12T23:02:42.982Z","repository":{"id":256731613,"uuid":"856247411","full_name":"Md-Emon-Hasan/MLFlow-Basic","owner":"Md-Emon-Hasan","description":"Covers essential features like model tracking, versioning, and experiment, providing a foundation for efficient ML project lifecycle management.","archived":false,"fork":false,"pushed_at":"2024-09-13T11:50:52.000Z","size":6477,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-13T08:46:19.019Z","etag":null,"topics":["experiment-tracking","mlflow","mlfq","mlops","model-lifecycle","model-tracking","model-versioning"],"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/Md-Emon-Hasan.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":"2024-09-12T08:46:07.000Z","updated_at":"2024-09-14T16:43:15.000Z","dependencies_parsed_at":"2024-09-12T20:01:19.929Z","dependency_job_id":"d46a13e6-c58b-48ad-a63b-b9d74c8961cb","html_url":"https://github.com/Md-Emon-Hasan/MLFlow-Basic","commit_stats":null,"previous_names":["md-emon-hasan/mlflow-basic"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Md-Emon-Hasan%2FMLFlow-Basic","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Md-Emon-Hasan%2FMLFlow-Basic/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Md-Emon-Hasan%2FMLFlow-Basic/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Md-Emon-Hasan%2FMLFlow-Basic/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Md-Emon-Hasan","download_url":"https://codeload.github.com/Md-Emon-Hasan/MLFlow-Basic/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241577071,"owners_count":19984940,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["experiment-tracking","mlflow","mlfq","mlops","model-lifecycle","model-tracking","model-versioning"],"created_at":"2024-11-13T20:38:58.514Z","updated_at":"2025-11-28T23:05:06.762Z","avatar_url":"https://github.com/Md-Emon-Hasan.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MLFlow Basic: Simplified Model Management\n\nWelcome to the **MLFlow Basic** repository! This project showcases a foundational approach to managing machine learning models using MLFlow. It demonstrates model tracking, versioning, and experiment management in a streamlined manner.\n\n## 📋 Contents\n\n- [Introduction](#introduction)\n- [Topics Covered](#topics-covered)\n- [Getting Started](#getting-started)\n- [Live Demo](#live-demo)\n- [MLFlow Integration](#mlflow-integration)\n- [Best Practices](#best-practices)\n- [FAQ](#faq)\n- [Troubleshooting](#troubleshooting)\n- [Contributing](#contributing)\n- [Additional Resources](#additional-resources)\n- [Challenges Faced](#challenges-faced)\n- [Lessons Learned](#lessons-learned)\n- [Why I Created This Repository](#why-i-created-this-repository)\n- [License](#license)\n- [Contact](#contact)\n\n---\n\n## 📖 Introduction\n\nThis repository introduces MLFlow, a tool for managing the lifecycle of machine learning models. It covers model tracking, versioning, and experimentation, providing a basic yet effective framework for managing ML projects.\n\n---\n\n## 🔍 Topics Covered\n\n- **Model Tracking:** Logging and tracking experiments and models.\n- **Versioning:** Managing different versions of models and their parameters.\n- **Experiment Management:** Organizing and comparing various experiments.\n- **MLFlow Integration:** Implementing MLFlow within machine learning workflows.\n\n---\n\n## 🚀 Getting Started\n\nTo get started with this project, follow these steps:\n\n1. **Clone the repository:**\n\n   ```bash\n   git clone https://github.com/Md-Emon-Hasan/MLFlow-Basic.git\n   ```\n\n2. **Navigate to the project directory:**\n\n   ```bash\n   cd MLFlow-Basic\n   ```\n\n3. **Create a virtual environment and activate it:**\n\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`\n   ```\n\n4. **Install the dependencies:**\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n5. **Run the application:**\n\n   ```bash\n   python main.py\n   ```\n\n6. **Open your browser and visit:**\n\n   ```\n   http://127.0.0.1:5000/\n   ```\n\n---\n\n## 🛠️ MLFlow Integration\n\n### MLFlow Basics\n\nThis project uses MLFlow for managing machine learning models. Key features include:\n\n1. **Tracking Experiments:** Record parameters, metrics, and models during experiments.\n2. **Version Control:** Maintain different versions of models for easy retrieval.\n3. **Model Deployment:** Register and deploy models using MLFlow’s model management tools.\n\n### Setup MLFlow\n\n1. **Start MLFlow server:**\n\n   ```bash\n   mlflow ui\n   ```\n\n2. **Access the MLFlow dashboard:**\n\n   ```\n   http://127.0.0.1:5000\n   ```\n\n---\n\n## 🌟 Best Practices\n\nRecommendations for maintaining and improving this project:\n\n- **Experiment Tracking:** Regularly track experiments to monitor progress and performance.\n- **Model Versioning:** Use MLFlow’s versioning to manage different stages of your models.\n- **Documentation:** Keep your MLFlow setup and usage well-documented.\n\n---\n\n## ❓ FAQ\n\n**Q: What is the purpose of this project?**\nA: This project demonstrates the basics of using MLFlow for model tracking, versioning, and experiment management.\n\n**Q: How can I contribute to this repository?**\nA: Refer to the [Contributing](#contributing) section for details on how to contribute.\n\n**Q: Can I integrate MLFlow with other ML frameworks?**\nA: Yes, MLFlow supports integration with various ML frameworks like TensorFlow, PyTorch, and Scikit-Learn.\n\n---\n\n## 🛠️ Troubleshooting\n\nCommon issues and solutions:\n\n- **Issue: MLFlow Server Not Starting**\n  *Solution:* Ensure that MLFlow is installed correctly and there are no port conflicts.\n\n- **Issue: Data Not Being Logged**\n  *Solution:* Verify that logging calls are placed correctly in your code and that the MLFlow server is running.\n\n- **Issue: Model Versioning Issues**\n  *Solution:* Check your MLFlow version and ensure that you’re using the correct commands for version management.\n\n---\n\n## 🤝 Contributing\n\nContributions are welcome! Here's how you can contribute:\n\n1. **Fork the repository.**\n2. **Create a new branch:**\n\n   ```bash\n   git checkout -b feature/new-feature\n   ```\n\n3. **Make your changes:**\n\n   - Add features, fix bugs, or improve documentation.\n\n4. **Commit your changes:**\n\n   ```bash\n   git commit -am 'Add a new feature or update'\n   ```\n\n5. **Push to the branch:**\n\n   ```bash\n   git push origin feature/new-feature\n   ```\n\n6. **Submit a pull request.**\n\n---\n\n## 📚 Additional Resources\n\nExplore these resources for more insights into MLFlow and model management:\n\n- **MLFlow Documentation:** [mlflow.org](https://mlflow.org/docs/latest/index.html)\n- **Machine Learning Lifecycle Management:** [MLFlow Guide](https://mlflow.org/docs/latest/quickstart.html)\n- **Model Versioning:** [Versioning with MLFlow](https://mlflow.org/docs/latest/model-registry.html)\n\n---\n\n## 💪 Challenges Faced\n\nSome challenges during development:\n\n- Integrating MLFlow with different ML frameworks.\n- Ensuring consistent logging and tracking across various experiments.\n- Managing and retrieving different model versions effectively.\n\n---\n\n## 📚 Lessons Learned\n\nKey takeaways from this project:\n\n- Gained experience in using MLFlow for model management.\n- Learned the importance of tracking and versioning in machine learning projects.\n- Developed an understanding of integrating MLFlow with different ML workflows.\n\n---\n\n## 🌟 Why I Created This Repository\n\nThis repository was created to provide a basic introduction to MLFlow, focusing on model tracking, versioning, and experiment management. It aims to simplify the initial setup and usage of MLFlow for newcomers.\n\n---\n\n## 📝 License\n\nThis repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details.\n\n---\n\n## 📬 Contact\n\n- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)\n- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)\n- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)\n- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)\n- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)\n\n---\n\nFeel free to adjust and expand this template based on your specific needs and project details!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmd-emon-hasan%2Fmlflow-basic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmd-emon-hasan%2Fmlflow-basic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmd-emon-hasan%2Fmlflow-basic/lists"}