SageMaker Studio Lab Examples
The SageMaker Studio Lab Examples repository offers a compelling suite of Jupyter notebooks designed to guide individuals on their journey to becoming skilled AI/ML practitioners using the Amazon SageMaker Studio Lab. From setting up a machine learning environment to deploying projects, this resource is free and accessible to anyone eager to explore machine learning.
Background
Amazon SageMaker Studio Lab is crafted for individual data scientists wishing to advance their careers in AI and ML. It creates opportunities to dive into machine learning without financial barriers. This repository walks users through configuring Studio Lab tailored to various interests like computer vision, natural language processing, and more. It also provides pathways for deploying projects to the broader Amazon SageMaker platform.
Setup
To get started with SageMaker Studio Lab, follow the onboarding process:
- Request and create a Studio Lab account.
- Sign in to Studio Lab.
For those preferring a customized user interface, localization instructions are available.
Usage
The repository is not only a resource for learning but also a dynamic platform for engagement:
- Read: View the notebooks directly in Studio Lab without requiring an account by using the "Open in Studio Lab" feature.
- Run: Execute the notebooks by copying or cloning the repository into your Studio Lab project.
- Share: Distribute your projects via Git repositories like GitHub. By adding an "Open in Studio Lab" button, others can easily access your work.
Notebooks Examples
Computer Vision
- Train an Image Classification Model with PyTorch: This notebook utilizes PyTorch to train models.
- Weather Classification with DenseNet-161: Focused on using DenseNet-161 for disaster risk reduction.
Natural Language Processing
- Finetune T5 for Machine Translation: Offers insight into using Hugging Face to finetune models on COVID-19 data.
Geospatial Data Science
- Introduction to Geospatial Data Analysis: A primer on handling geospatial data.
- Exploratory Analysis for NOAA Weather Dataset: Insights into analyzing weather and climate data.
Generative Deep Learning
- JumpStart - Text to Image: Guides on utilizing concepts to transition text to images.
- Prompting Mistral 7B Instruct: Focus on generating prompts for models.
Connect To AWS
- Using SageMaker Studio Lab with AWS Resources: Explore integration with AWS.
- Deploy a Pretrained Model to AWS: Instructions on deploying models using Amazon SageMaker serverless endpoint.
Custom Environments
The repository provides .yml files for setting up various programming environments, catering to languages and frameworks like R, Julia, AutoGluon, fast.ai, SciPy, diffusers, RAPIDS, geospatial analysis, and more. Detailed steps ensure effortless environment building and checking.
Community Content
Further examples and community-driven content can be accessed through GitHub using the amazon-sagemaker-lab tag. Notable contributions are showcased either in the repository or associated blogs.
License & Contribution
The project is openly accessible under the Apache-2.0 License. While contributions are welcome, the process is still evolving, and the team appreciates patience as they optimize their system for incorporating external contributions.
References
For more information, users can refer to the SageMaker Studio Lab main site and other related documentation, along with community forums such as Stack Overflow.
This comprehensive array of resources and instructional content makes the SageMaker Studio Lab Examples repository a potent tool for budding and experienced data scientists alike.