{"id":22312206,"url":"https://github.com/ajejoseph22/supreme-funicular","last_synced_at":"2026-04-14T04:03:19.310Z","repository":{"id":121274150,"uuid":"527649911","full_name":"ajejoseph22/supreme-funicular","owner":"ajejoseph22","description":"Machine Learning Microservice in Docker and Kubernetes.","archived":false,"fork":false,"pushed_at":"2022-08-24T21:29:05.000Z","size":225,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-09-25T18:44:16.763Z","etag":null,"topics":["aws","bash","continuous-integration","docker","kubernetes","python3"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ajejoseph22.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2022-08-22T16:41:42.000Z","updated_at":"2024-12-23T15:19:40.000Z","dependencies_parsed_at":null,"dependency_job_id":"52a02682-5fa8-40fa-ac1d-db94654e0ed1","html_url":"https://github.com/ajejoseph22/supreme-funicular","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ajejoseph22/supreme-funicular","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ajejoseph22%2Fsupreme-funicular","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ajejoseph22%2Fsupreme-funicular/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ajejoseph22%2Fsupreme-funicular/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ajejoseph22%2Fsupreme-funicular/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ajejoseph22","download_url":"https://codeload.github.com/ajejoseph22/supreme-funicular/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ajejoseph22%2Fsupreme-funicular/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":276966323,"owners_count":25736756,"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","status":"online","status_checked_at":"2025-09-25T02:00:09.612Z","response_time":80,"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":["aws","bash","continuous-integration","docker","kubernetes","python3"],"created_at":"2024-12-03T21:35:34.869Z","updated_at":"2025-09-25T18:44:18.981Z","avatar_url":"https://github.com/ajejoseph22.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"Circle CI status badge: \n\n[![ajejoseph22](https://circleci.com/gh/ajejoseph22/supreme-funicular.svg?style=svg)](https://app.circleci.com/pipelines/github/ajejoseph22/supreme-funicular/)\n\n## Project Overview\n\nIn this project, I operationalized a Machine Learning microservice API, containerized it and deployed to a Kubernetes cluster. I also set up continuous integration on Circle CI.\n\nThis project contains a `sklearn` model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. You can read more about the data, which was initially taken from Kaggle, on [the data source site](https://www.kaggle.com/c/boston-housing). This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.\n\n---\n\n## Installation steps\n* Clone the repository\n\nTo run in `Docker`, you need to have `Docker` installed and running on the host and then:\n* Run `bash run_docker.sh`. This would build the image using the `Dockerfile` and run the container on your host, forwarding traffic from port `80` in the container to post `8080` on your host.\n\nTo run in a Kubernetes pod, you need to have `minikube` installed and started on the host. `minikube` requires a virtualization tool such as [virtual box](https://www.virtualbox.org/), so you need to install that too. You can start `minikube` using `minikube start` and make use of the default cluster for deployments.:\n* Run `bash run_kubernetes.sh`. This will run the container in a Kubernetes pod and forward traffic from port `80` in the container to post `8080` on your host \n\nThat's it\n\n### Calling the prediction API\n* Run `bash make_prediction.sh`\n\n## Running without containerization\n* Install python 3.7\n* Run `make setup` to set up a venv and activate it\n* Install dependencies with `make install`\n* To lint the code run `make lint`\n* Run the server with `python3 app.py`\n* Change `$PORT` in `make_prediction.sh` to `80`  and run `bash make_prediction.sh` to call the API\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fajejoseph22%2Fsupreme-funicular","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fajejoseph22%2Fsupreme-funicular","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fajejoseph22%2Fsupreme-funicular/lists"}