{"id":21514869,"url":"https://github.com/getindata/quickstart-ml-starter","last_synced_at":"2025-10-30T15:08:51.106Z","repository":{"id":137895558,"uuid":"599571857","full_name":"getindata/quickstart-ml-starter","owner":"getindata","description":"  Kedro starterts to quickly set up new projects according to QuickStart ML Blueprints practice.","archived":false,"fork":false,"pushed_at":"2023-04-05T14:10:31.000Z","size":4749,"stargazers_count":5,"open_issues_count":0,"forks_count":3,"subscribers_count":6,"default_branch":"main","last_synced_at":"2025-01-24T02:30:28.265Z","etag":null,"topics":["data-science","machine-learning"],"latest_commit_sha":null,"homepage":"","language":null,"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/getindata.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":"2023-02-09T12:29:49.000Z","updated_at":"2023-10-12T15:07:51.000Z","dependencies_parsed_at":"2023-06-07T22:00:23.756Z","dependency_job_id":null,"html_url":"https://github.com/getindata/quickstart-ml-starter","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/getindata%2Fquickstart-ml-starter","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/getindata%2Fquickstart-ml-starter/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/getindata%2Fquickstart-ml-starter/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/getindata%2Fquickstart-ml-starter/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/getindata","download_url":"https://codeload.github.com/getindata/quickstart-ml-starter/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244066191,"owners_count":20392407,"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":["data-science","machine-learning"],"created_at":"2024-11-23T23:53:20.334Z","updated_at":"2025-10-30T15:08:51.014Z","avatar_url":"https://github.com/getindata.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# QuickStart ML Kedro starter\n\n## Overview\n\nThis is a set of [Cookiecutter](https://www.cookiecutter.io/) templates in the form of [Kedro starters](https://kedro.readthedocs.io/en/0.18.0/get_started/starters.html). These starters allow to easily create a new project that doesn't implement any nodes or pipelines yet, but contains necessary tooling and follows all [QuickStart ML Blueprints](https://github.com/getindata/quickstart-ml-blueprints) principles.\n\nQuickStart ML Blueprints repository and documentation with detailed description of the way of work can be found [here](https://github.com/getindata/quickstart-ml-blueprints).\n\nInitiating a project using one of the Kedro starters you will get out-of-the box:\n* appropriate project structure matching [Kedro](https://kedro.org/) and Cookiecutter standard that features configuration files, code testing framework, layered data-engineering convention and more\n* [VSCode Dev Containers](https://code.visualstudio.com/docs/devcontainers/containers) and Docker setup files to create a transferrable working environment automatically\n* [MLFlow](https://mlflow.org/) and [Kedro-Viz](https://docs.kedro.org/en/0.17.4/03_tutorial/06_visualise_pipeline.html)\n* A set of pre-configures environment management and code quality tools ([Poetry](https://python-poetry.org/), [pre-commit](https://pre-commit.com/) hooks, linters)\n* Accordingly to your target full-scale environment - Kedro plugins setup for easy transfer and running your local work on [GCP](https://github.com/getindata/kedro-vertexai), [AWS](https://github.com/getindata/kedro-sagemaker),  [Azure](https://github.com/getindata/kedro-azureml) or [Kubeflow](https://github.com/getindata/kedro-kubeflow)\n\nThere are a few branches in the repository that use basically the same template, but have environment-specific additions depending on where are you planning to run your full-scale solution after local prototyping phase:\n- `local` - if you plan to stay in local environment\n- `local-gcp` - if you plan to transfer your project to Google Cloud (VertexAI)\n- (to be added ) `local-aws` - if you plan to transfer your project to AWS (Sagemaker)\n- (to be added ) `local-azure` - if you plan to transfer your project to Azure (AzureML)\n- (to be added ) `local-kuberflow` - if you plan to transfer your project to Kubeflow\n\n## Usage\n\nTo use this Kedro starter you to have some Python 3 environment with Kedro installed. The method of installation is up to you (you can use Pyenv and Poetry, Conda, Virtual Env etc.) - this installation Kedro is only needed to create a project from a starter. After that, the project will use its own encapsulated Pyenv/Poetry environment with its own Kedro.\n\nTo create a new project using Kedro starter:\n\n```bash\n# For HTTPS cloning:\nkedro new --starter=https://github.com/getindata/quickstart-ml-starter.git --checkout=\u003cbranch_name\u003e\n\n# For SSH cloning:\nkedro new --starter=git@github.com:getindata/quickstart-ml-starter.git  --checkout=\u003cbranch_name\u003e\n\n# Follow the prompts to name your project and optionally set cloud project details, then change directory into newly created project directory:\ncd \u003cmy-project-name\u003e\n```\n\nAfter that, follow the way of work described in [QuickStart ML Blueprints](https://github.com/getindata/quickstart-ml-blueprints) to develop your project.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgetindata%2Fquickstart-ml-starter","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgetindata%2Fquickstart-ml-starter","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgetindata%2Fquickstart-ml-starter/lists"}