{"id":18990923,"url":"https://github.com/datadrivers/effective-guide-mlops","last_synced_at":"2026-03-08T04:31:39.727Z","repository":{"id":103176295,"uuid":"399397303","full_name":"datadrivers/effective-guide-mlops","owner":"datadrivers","description":"Example end-to-end ml pipeline build with the Sagemaker Python SDK","archived":false,"fork":false,"pushed_at":"2021-12-16T14:36:11.000Z","size":502,"stargazers_count":4,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-09-06T05:40:52.253Z","etag":null,"topics":["aws","aws-api-gateway","aws-apigateway","aws-sagemaker","data-science","deep-learning","machine-learning","mlops","mlops-environment","mlops-workflow","python","scikit-learn","scikitlearn-machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/datadrivers.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":"2021-08-24T08:52:20.000Z","updated_at":"2023-07-26T14:06:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"8ce39909-a5be-4cd7-92a9-c97a23e587c0","html_url":"https://github.com/datadrivers/effective-guide-mlops","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/datadrivers/effective-guide-mlops","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datadrivers%2Feffective-guide-mlops","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datadrivers%2Feffective-guide-mlops/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datadrivers%2Feffective-guide-mlops/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datadrivers%2Feffective-guide-mlops/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/datadrivers","download_url":"https://codeload.github.com/datadrivers/effective-guide-mlops/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datadrivers%2Feffective-guide-mlops/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30245217,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-08T00:58:18.660Z","status":"online","status_checked_at":"2026-03-08T02:00:06.215Z","response_time":56,"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","aws-api-gateway","aws-apigateway","aws-sagemaker","data-science","deep-learning","machine-learning","mlops","mlops-environment","mlops-workflow","python","scikit-learn","scikitlearn-machine-learning"],"created_at":"2024-11-08T17:12:09.422Z","updated_at":"2026-03-08T04:31:39.707Z","avatar_url":"https://github.com/datadrivers.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# effective-guide-mlops\n#### End-to-end machine learning pipeline with Sagemaker Python SDK\n\n\u003cp float=\"left\"\u003e\n\u003cimg src=\"https://looker.com/assets/img/images/logos/external/bricks/amazon_sagemaker.png\" alt=\"Sagemaker\" height=\"200\"/\u003e\n\u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Python-logo-notext.svg/1200px-Python-logo-notext.svg.png\" alt=\"Python\" height=\"200\"/\u003e\n\u003c/p\u003e\n\nThis repository provides an example end-to-end machine learning pipeline on AWS build using the Sagemaker Python SDK. It leans on other resources (e.g. [here](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_processing/scikit_learn_data_processing_and_model_evaluation/scikit_learn_data_processing_and_model_evaluation.ipynb) and [here](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html)), however, it provides a unified end-to-end example in a notebook from data processing to deployment of a REST API. This not production ready, but it will give you a good primary intuition how to orchestrate the ml lifecycle on AWS via the Sagemaker SDK. \n\nThe main ressource for this guid is the notebook `ml_pipeline.ipynb` in the folder `notebooks`. The easiest way to follow along the tutorial would be to launch a notebook instance on AWS Sagemaker and pull the repository into your jupyterlab environment. \n\n\n### 1. Data\nThe [Penguin Dataset](https://allisonhorst.github.io/palmerpenguins/articles/intro.html) from Alison Horst is an alternative to the famous iris dataset that can be used for demonstrating various ml tasks. \nRead more [here](https://allisonhorst.github.io/palmerpenguins/articles/intro.html).\n![Penguins](https://allisonhorst.github.io/palmerpenguins/man/figures/lter_penguins.png)\n\n\n|    | species   | island    |   bill_length_mm |   bill_depth_mm |   flipper_length_mm |   body_mass_g | sex    |   year |\n|----|-----------|-----------|------------------|-----------------|---------------------|---------------|--------|--------|\n|  1 | Adelie    | Torgersen |             39.1 |            18.7 |                 181 |          3750 | male   |   2007 |\n|  2 | Adelie    | Torgersen |             39.5 |            17.4 |                 186 |          3800 | female |   2007 |\n|  3 | Adelie    | Torgersen |             40.3 |            18   |                 195 |          3250 | female |   2007 |\n\n\n### 2. Objective\n\nThe goal is to train a classifier that predicts the sex/gender of a penguin based on all other variables available.\n\n### 3. Ressources\n\n##### Notebooks:\n\n- stored in `/notebooks`\n- `eda.ipynb` visual exploration of the data\n- `ml_pipeline.ipynb` orchestrates preprocessing of the data, model training and deployment of the model as endpoint\n\n### 4 Tutorial Wolkthrough\n\n- head over to `notebooks.ml_pipeline.ipynb` and follow the procedure\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatadrivers%2Feffective-guide-mlops","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatadrivers%2Feffective-guide-mlops","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatadrivers%2Feffective-guide-mlops/lists"}