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https://github.com/iterative/awesome-iterative-projects

A list of projects relying on Iterative.AI tools to achieve awesomeness
https://github.com/iterative/awesome-iterative-projects

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A list of projects relying on Iterative.AI tools to achieve awesomeness

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[![](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)



# Awesome Iterative Projects



[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



A list of projects relying on [Iterative](https://github.com/iterative) tools to achieve awesomeness.



Missing something awesome? Anyone is welcome to [submit projects to this list](https://github.com/iterative/awesome-iterative-projects/blob/main/contributing.md).



## Tools & Libraries

* [`dvthis`](https://github.com/jcpsantiago/dvthis): Utility functions and project templates for DVC pipelines using R.

* [nvim-dvc](https://github.com/gennaro-tedesco/nvim-dvc): Neovim plugin for DVC.

* [COVID Genomics/Airflow-DVC](https://github.com/covid-genomics/airflow-dvc): Airflow extension for DVC.

* [COVID Genomics/dvc-fs](https://github.com/covid-genomics/dvc-fs): High-level abstraction for DVC file manipulation (listing & I/O) with basic support for [PyFilesystem2](https://github.com/PyFilesystem/pyfilesystem2).

* [zincware/ZnTrack](https://github.com/zincware/ZnTrack): Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.

* [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) 



## Tutorials

* [DVC Streamlit Example](https://github.com/sicara/dvc-streamlit-example): Build a custom web UI with DVC and Streamlit for visually tracking & comparing model performance during R&D (adapted from [TensorFlow's transfer learning tutorial](https://www.tensorflow.org/tutorials/images/transfer_learning)).

* [DVC Pipelines and Experiments Tutorial](https://github.com/dmesquita/dvc_pipelines_and_experiments_tutorial): Build maintainable Machine Learning pipelines using DVC.

* [CD4ML Example](https://github.com/sbalnojan/cd4ml-example): Example DVC setup with AWS S3 remote storage & GitLab CI/CD.

* [DVC with PyCaret & FastAPI](https://github.com/tezansahu/dvc-pycaret-fastapi-demo): End-to-end demo of data & model tracking (DVC), remote storage (Azure), efficient experimentation (PyCaret) & model deployment (FastAPI).



### Iterative

* [Example-get-started](https://github.com/iterative/example-get-started): Train a `sklearn` random forest classifier for StackOverflow question tagging.

* [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.

* [Example-versioning](https://github.com/iterative/example-versioning): Used in https://dvc.org/doc/use-cases/versioning-data-and-model-files/tutorial.

* [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.

* [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.



## Real-world Projects

* [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 & training, and Jupyter notebooks.

* [ModelOriented/MAIR](https://github.com/ModelOriented/MAIR): Monitoring impact of AI regulations with a DVC pipeline.

* [Kaggle-Titanic-DVC](https://dagshub.com/kingabzpro/kaggle-titanic-dvc): Survival analysis DVC experiment.

* [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.

* [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.



## Research Papers

* 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.



* 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.