Amazon SageMaker Community Notebooks

Welcome to the Amazon SageMaker Community Notebooks repository! This repository is a sibling of official sagemaker examples repository where the official sagemaker examples repository demonstrates only the core capabilities of Amazon SageMaker, while this repository covers additional examples which may go beyond SageMaker or may cover more advanced examples combining multiple features/services of AWS together.

Note able to submit a cool SageMaker example to official SageMaker repository because it did not meet the contributing criteria there? Feel free to submit a PR to this repository instead !!

📚 Purpose

Amazon SageMaker is a powerful tool for simplifying machine learning workflows, from data preprocessing to model deployment. However, the journey of mastering SageMaker often involves experimentation, creative problem-solving, and the exploration of unique approaches that might not fit the standard showcase format. This community repository is here to accommodate such scenarios by hosting a collection of notebooks that might not fit the conventional mold but still provide valuable insights, alternative techniques, and unconventional applications of SageMaker.

🚀 How to Contribute

We welcome contributions from the community! If you have a notebook that you believe would benefit others, whether it's an innovative workaround, a specialized use case, or a creative visualization, feel free to share it here. To contribute:

Fork this repository to your own GitHub account. Create a new branch from the main branch for your changes. Add your notebook to an appropriate directory or create a new directory if needed. Craft a comprehensive README for your notebook, explaining its purpose, contents, and any necessary setup instructions. Submit a pull request back to this repository's main branch for review. Remember, the focus here is not just on showcasing SageMaker's core features, but on fostering a community-driven space for learning, sharing, and discovering innovative applications of machine learning with SageMaker.

💻 Exploration

To explore the notebooks in this repository:

Clone or Download: You can clone this repository to your local machine or download specific notebooks as needed.

Notebook Instances: If you have an Amazon SageMaker Notebook Instance, you can upload these community notebooks and experiment with them directly in the SageMaker environment.

Local Setup: For notebooks that don't require extensive SageMaker-specific functionality, you can often run them on your local machine with minimal modifications. Just ensure you have the necessary libraries installed and, if needed, update IAM role definitions accordingly.

📝 Note

While the primary SageMaker Example Notebooks repository is carefully curated to demonstrate key functionality, the notebooks in this community repository are more open-ended and diverse. They may not undergo the same level of scrutiny, and it's important to exercise caution and understand the code you're using.

As of now, the default branch is named main. Please refer to our announcements for any updates or changes.

Thank you for being part of the Amazon SageMaker community! Together, we can explore the full spectrum of machine learning possibilities this platform has to offer.