{"id":25830992,"url":"https://github.com/prsdm/mlops-project","last_synced_at":"2025-04-05T13:01:42.272Z","repository":{"id":255346996,"uuid":"844131778","full_name":"prsdm/mlops-project","owner":"prsdm","description":null,"archived":false,"fork":false,"pushed_at":"2024-11-18T11:46:18.000Z","size":9832,"stargazers_count":119,"open_issues_count":1,"forks_count":58,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-29T12:02:38.593Z","etag":null,"topics":["ai","awsecs","data-science","docker","dvc","evidentlyai","fastapi","machine-learning","machinelearning","mlflow","mlops","mlops-project","mlops-workflow"],"latest_commit_sha":null,"homepage":"https://prsdm.github.io/mlops-project/","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/prsdm.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","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},"funding":{"github":"prsdm"}},"created_at":"2024-08-18T13:35:20.000Z","updated_at":"2025-03-28T04:06:06.000Z","dependencies_parsed_at":"2024-08-29T12:39:30.003Z","dependency_job_id":"50941c1e-d7d5-436b-a9cd-f25ea164bb70","html_url":"https://github.com/prsdm/mlops-project","commit_stats":null,"previous_names":["prsdm/ml-project","prsdm/mlops-project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prsdm%2Fmlops-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prsdm%2Fmlops-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prsdm%2Fmlops-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prsdm%2Fmlops-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/prsdm","download_url":"https://codeload.github.com/prsdm/mlops-project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247339145,"owners_count":20923013,"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":["ai","awsecs","data-science","docker","dvc","evidentlyai","fastapi","machine-learning","machinelearning","mlflow","mlops","mlops-project","mlops-workflow"],"created_at":"2025-02-28T19:37:20.768Z","updated_at":"2025-04-05T13:01:42.220Z","avatar_url":"https://github.com/prsdm.png","language":"HTML","funding_links":["https://github.com/sponsors/prsdm"],"categories":[],"sub_categories":[],"readme":"# Insurance Cross Sell Prediction 🏠🏥\n[![GitHub](https://img.shields.io/badge/GitHub-code-blue?style=flat\u0026logo=github\u0026logoColor=white\u0026color=red)](https://github.com/prsdm/mlops-project) [![Medium](https://img.shields.io/badge/Medium-view_article-green?style=flat\u0026logo=medium\u0026logoColor=white\u0026color=green)](https://medium.com/@prasadmahamulkar/machine-learning-operations-mlops-for-beginners-a5686bfe02b2)\n\nWelcome to the Insurance Cross-Selling Prediction project! The goal of this project is to predict which customers are most likely to purchase additional insurance products using a machine learning model.\n\n\n## Diagram\nBelow is the architecture diagram that illustrates the flow of the project from data ingestion to model deployment:\n![Image](docs/mlops.jpg)\n\n## Get Started\nTo get started with the project, follow the steps below:\n\n#### 1. Clone the Repository\nClone the project repository from GitHub:\n```bash\ngit clone https://github.com/prsdm/ml-project.git\n```\n```bash\ncd ml-project\n```\n#### 2. Set Up the Environment\nEnsure you have Python 3.8+ installed. Create a virtual environment and install the necessary dependencies:\n```bash\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\npip install -r requirements.txt\n```\nAlternatively, you can use the Makefile command:\n```bash\nmake setup\n```\n#### 3. Data Preparation\nPull the data from DVC. If this command doesn't work, the train and test data are already present in the data folder:\n```bash\ndvc pull\n```\n\n#### 4. Train the Model\nTo train the model, run the following command:\n\n```bash\npython main.py \n```\nOr use the Makefile command:\n\n```bash\nmake run\n```\nThis script will load the data, preprocess it, train the model, and save the trained model to the models/ directory.\n\n#### 5. FastAPI\nStart the FastAPI application by running:\n\n```bash\nuvicorn app:app --reload\n```\n\n#### 6. Docker\nTo build the Docker image and run the container:\n\n```bash\ndocker build -t my_fastapi .\n```\n```bash\ndocker run -p 80:80 my_fastapi\n```\nOnce your Docker image is built, you can push it to Docker Hub, making it accessible for deployment on any cloud platform.\n#### 7. Monitor the Model\nIntegrate Evidently AI to monitor the model for data drift and performance degradation:\n\n```bash\nrun monitor.ipynb file\n```\n\n## License\n\nCopyright © 2024, [Prasad Mahamulkar](https://github.com/prsdm).\n\nReleased under the [Apache-2.0 license](LICENSE).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprsdm%2Fmlops-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprsdm%2Fmlops-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprsdm%2Fmlops-project/lists"}