{"id":13684918,"url":"https://github.com/iterative/awesome-iterative-projects","last_synced_at":"2025-06-18T02:39:57.014Z","repository":{"id":39910493,"uuid":"212457372","full_name":"iterative/awesome-iterative-projects","owner":"iterative","description":"A list of projects relying on Iterative.AI tools to achieve awesomeness","archived":false,"fork":false,"pushed_at":"2024-07-11T11:34:41.000Z","size":40,"stargazers_count":66,"open_issues_count":1,"forks_count":7,"subscribers_count":15,"default_branch":"main","last_synced_at":"2025-06-06T05:11:48.396Z","etag":null,"topics":["awesome","awesome-dvc","awesome-list","awesome-lists","data-science","deep-learning","dvc","example","hacktoberfest","machine-learning"],"latest_commit_sha":null,"homepage":"https://iterative.ai","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/iterative.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":"contributing.md","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":"2019-10-02T23:03:47.000Z","updated_at":"2024-12-20T04:32:52.000Z","dependencies_parsed_at":"2024-01-05T21:43:45.636Z","dependency_job_id":"923c60ab-8bd1-47c5-a06d-b66573299b09","html_url":"https://github.com/iterative/awesome-iterative-projects","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/iterative/awesome-iterative-projects","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iterative%2Fawesome-iterative-projects","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iterative%2Fawesome-iterative-projects/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iterative%2Fawesome-iterative-projects/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iterative%2Fawesome-iterative-projects/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iterative","download_url":"https://codeload.github.com/iterative/awesome-iterative-projects/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iterative%2Fawesome-iterative-projects/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259599313,"owners_count":22882349,"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":["awesome","awesome-dvc","awesome-list","awesome-lists","data-science","deep-learning","dvc","example","hacktoberfest","machine-learning"],"created_at":"2024-08-02T14:00:40.607Z","updated_at":"2025-06-18T02:39:52.000Z","avatar_url":"https://github.com/iterative.png","language":null,"funding_links":[],"categories":["Other awesome AI lists"],"sub_categories":[],"readme":"[![](https://static.iterative.ai/logo/enterprise.svg)](https://iterative.ai) [![](https://static.iterative.ai/logo/dvc.svg)](https://dvc.org) [![](https://static.iterative.ai/logo/cml.svg)](https://cml.dev) [![](https://static.iterative.ai/logo/mlem.svg)](https://mlem.ai) [![](https://static.iterative.ai/logo/studio.svg)](https://studio.iterative.ai)\n\n# Awesome Iterative Projects\n\n[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n\nA list of projects relying on [Iterative](https://github.com/iterative) tools to achieve awesomeness.\n\nMissing something awesome? Anyone is welcome to [submit projects to this list](https://github.com/iterative/awesome-iterative-projects/blob/main/contributing.md).\n\n## Tools \u0026 Libraries\n* [`dvthis`](https://github.com/jcpsantiago/dvthis): Utility functions and project templates for DVC pipelines using R.\n* [nvim-dvc](https://github.com/gennaro-tedesco/nvim-dvc): Neovim plugin for DVC.\n* [COVID Genomics/Airflow-DVC](https://github.com/covid-genomics/airflow-dvc): Airflow extension for DVC.\n* [COVID Genomics/dvc-fs](https://github.com/covid-genomics/dvc-fs): High-level abstraction for DVC file manipulation (listing \u0026 I/O) with basic support for [PyFilesystem2](https://github.com/PyFilesystem/pyfilesystem2).\n* [zincware/ZnTrack](https://github.com/zincware/ZnTrack): Create, visualize, run \u0026 benchmark DVC pipelines in Python \u0026 Jupyter notebooks.\n* [zincware/dask4dvc](https://github.com/zincware/dask4dvc): Provides a DVC-like CLI that combines DVC with [Dask Distributed](https://distributed.dask.org/) to make it easier to use with HPC managers like [Slurm](https://github.com/SchedMD/slurm) \n\n## Tutorials\n* [DVC Streamlit Example](https://github.com/sicara/dvc-streamlit-example): Build a custom web UI with DVC and Streamlit for visually tracking \u0026 comparing model performance during R\u0026D (adapted from [TensorFlow's transfer learning tutorial](https://www.tensorflow.org/tutorials/images/transfer_learning)).\n* [DVC Pipelines and Experiments Tutorial](https://github.com/dmesquita/dvc_pipelines_and_experiments_tutorial): Build maintainable Machine Learning pipelines using DVC.\n* [CD4ML Example](https://github.com/sbalnojan/cd4ml-example): Example DVC setup with AWS S3 remote storage \u0026 GitLab CI/CD.\n* [DVC with PyCaret \u0026 FastAPI](https://github.com/tezansahu/dvc-pycaret-fastapi-demo): End-to-end demo of data \u0026 model tracking (DVC), remote storage (Azure), efficient experimentation (PyCaret) \u0026 model deployment (FastAPI).\n\n### Iterative\n* [Example-get-started](https://github.com/iterative/example-get-started): Train a `sklearn` random forest classifier for StackOverflow question tagging.\n* [Example-DVC-experiments](https://github.com/iterative/example-dvc-experiments): Train a Tensorflow CNN classifier for Fashion-MNIST data; used in https://dvc.org/doc/start/experiments.\n* [Example-versioning](https://github.com/iterative/example-versioning): Used in https://dvc.org/doc/use-cases/versioning-data-and-model-files/tutorial.\n* [DVC-Checkpoints-MNIST](https://github.com/iterative/dvc-checkpoints-mnist): A showcase for different ways to use the checkpoints. Train a PyTorch classifier on a CSV MNIST dataset.\n* [Scalable and Distributed ML Workflows with DVC + Ray on AWS](https://github.com/iterative/tutorial-mnist-dvc-ray): This tutorial introduces you to integrating DVC with Ray, turning them into your go-to toolkit for creating automated, scalable, and distributed ML pipelines.\n\n## Real-world Projects\n* [LensKit/lk-demo-experiment](https://github.com/lenskit/lk-demo-experiment): Demo DVC experiment [pipeline (DAG)](https://dvc.org/doc/user-guide/glossary#pipeline-DAG) using multiple public datasets, preprocessing \u0026 training, and Jupyter notebooks.\n* [ModelOriented/MAIR](https://github.com/ModelOriented/MAIR): Monitoring impact of AI regulations with a DVC pipeline.\n* [Kaggle-Titanic-DVC](https://dagshub.com/kingabzpro/kaggle-titanic-dvc): Survival analysis DVC experiment.\n* [VQA-With-Multimodal-Transformers](https://github.com/tezansahu/VQA-With-Multimodal-Transformers): Visual Question Answering task on the [DAQUAR Dataset](https://www.kaggle.com/tezansahu/processed-daquar-dataset) using multimodal [transformer models](https://huggingface.co/docs/transformers/index) with an experiment pipeline tracked in DVC Studio.\n* [pinellolab/pyrovelocity](https://github.com/pinellolab/pyrovelocity): `Pyro-Velocity` is a Bayesian, generative, and multivariate RNA velocity model to estimate _uncertainty_ in predictions of future cell states from minimal models approximating transcript splicing dynamics.\n\n## Research Papers\n* Barrak, A., Eghan, E.E. and Adams, B. [On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects](https://mcis.cs.queensu.ca/publications/2021/saner.pdf) , in Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2021. Hawaii, USA.\n\n* Barreto Simedo Pacheco, L., Rahman, M., Rabbi, F., Fathollahzadeh, P., Abdellatif, A., Shihab, E., Chen, T.P., Yang, J., and Zou, Y. [DVC in Open Source ML-development: The Action and the Reaction](https://dl.acm.org/doi/pdf/10.1145/3644815.3644965), In Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI (CAIN '24). Lisbon, Portugal.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiterative%2Fawesome-iterative-projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fiterative%2Fawesome-iterative-projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiterative%2Fawesome-iterative-projects/lists"}