https://github.com/outerbounds/tutorials
https://github.com/outerbounds/tutorials
Last synced: about 1 year ago
JSON representation
- Host: GitHub
- URL: https://github.com/outerbounds/tutorials
- Owner: outerbounds
- License: apache-2.0
- Created: 2022-07-18T19:51:52.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-06-07T20:31:24.000Z (about 2 years ago)
- Last Synced: 2025-04-02T04:23:31.545Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage: http://outerbounds.github.io/tutorials/
- Size: 116 MB
- Stars: 12
- Watchers: 4
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-metaflow - tutorials - Official tutorials on workflow graphs, versioning, data access, and scheduling. (Examples & Tutorials)
README
Welcome to the Metaflow Tutorials 👋
================
This repository contains the code to follow along with these [Metaflow tutorials](https://outerbounds.com/docs/tutorials-welcome/):
- [Introduction to Metaflow](https://outerbounds.com/docs/intro-tutorial-overview/)
- [Natural Language Processing with Metaflow](https://outerbounds.com/docs/nlp-tutorial-overview/)
- [Computer Vision with Metaflow - Beginner](https://outerbounds.com/docs/cv-tutorial-overview/)
- [Computer Vision with Metaflow - Intermediate](https://outerbounds.com/docs/cv-tutorial-S2-overview/)
- [Recommender Systems with Metaflow](https://outerbounds.com/docs/recsys-tutorial-overview/)
Metaflow is an open source machine learning infrastructure stack started at [Netflix](https://github.com/Netflix). Outerbounds is a company that maintains Metaflow and repositories like this that aim to help data scientists do their jobs.
The tutorials in this repository will teach you how to represent machine learning and data science [workflows as graphs](https://outerbounds.com/docs/dags-in-data-science/). You will see benefits of structuring workflows this way like versioning, access to data, and job scheduling on many cloud computers. By engaging in the tutorials, you will learn how to build domain-specific workflows that reliably run locally or in the cloud (with minimal set up). You will see many workflows with composable steps you can mix and match to your unique ML applications.
Join us on [Slack](http://slack.outerbounds.co/) and let us know how we can support your machine learning journey.