{"id":18339973,"url":"https://github.com/mxagar/mlops_udacity","last_synced_at":"2025-04-06T05:32:27.745Z","repository":{"id":43638599,"uuid":"490995350","full_name":"mxagar/mlops_udacity","owner":"mxagar","description":"These are my notes of the Udacity Nanodegree Machine Learning DevOps Engineer.","archived":false,"fork":false,"pushed_at":"2024-03-27T07:07:13.000Z","size":36893,"stargazers_count":9,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-21T18:11:15.320Z","etag":null,"topics":["cicd","deployment","dvc","fastapi","heroku","machine-learning","mlflow","mlops","model-drift","monitoring","production","wandb"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/mxagar.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}},"created_at":"2022-05-11T07:06:29.000Z","updated_at":"2024-12-30T22:26:57.000Z","dependencies_parsed_at":"2024-03-27T08:37:18.865Z","dependency_job_id":null,"html_url":"https://github.com/mxagar/mlops_udacity","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/mxagar%2Fmlops_udacity","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fmlops_udacity/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fmlops_udacity/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fmlops_udacity/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mxagar","download_url":"https://codeload.github.com/mxagar/mlops_udacity/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247440626,"owners_count":20939223,"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":["cicd","deployment","dvc","fastapi","heroku","machine-learning","mlflow","mlops","model-drift","monitoring","production","wandb"],"created_at":"2024-11-05T20:20:09.858Z","updated_at":"2025-04-06T05:32:22.731Z","avatar_url":"https://github.com/mxagar.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning DevOps Engineer: Personal Notes on the Udacity Nanodegree\n\nThese are my notes of the [Udacity Nanodegree Machine Learning DevOps Engineer](https://www.udacity.com/course/machine-learning-dev-ops-engineer-nanodegree--nd0821).\n\nThe nanodegree is composed of four modules:\n\n1. [Clean Code Principles](./01_Clean_Code/MLOpsND_CleanCode.md)\n2. [Building a Reproducible Model Workflow](./02_Reproducible_Pipelines/MLOpsND_ReproduciblePipelines.md)\n3. [Deploying a Scalable ML Pipeline in Production](./03_Deployment/MLOpsND_Deployment.md)\n4. [ML Model Scoring and Monitoring](./04_Monitoring/MLOpsND_Monitoring.md)\n\nEach module has a folder with its respective notes; **you need to go to each module folder and follow the Markdown file in it.**\n\n## Projects\n\nUdacity requires the submission of a project for each module; these are the repositories of the projects I submitted:\n\n1. Predicting Customer Churn with *Production-Level* Software: [customer_churn_production](https://github.com/mxagar/customer_churn_production).\n2. A Reproducible Machine Learning Pipeline for Short-Term Rental Price Prediction in New York City: [ml_pipeline_rental_prices](https://github.com/mxagar/ml_pipeline_rental_prices).\n3. Deploying a Machine Learning Model on Heroku with FastAPI: [census_model_deployment_fastapi](https://github.com/mxagar/census_model_deployment_fastapi).\n4. A Dynamic Risk Assessment System \u0026mdash; Monitoring of a Customer Churn Model: [churn_model_monitoring](https://github.com/mxagar/churn_model_monitoring).\n\nMikel Sagardia, 2022.  \nNo guarantees.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fmlops_udacity","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmxagar%2Fmlops_udacity","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fmlops_udacity/lists"}