This repository contains example notebooks that show how to use algorithms and model packages from AWS Marketplace for machine learning
To know more about algorithms and model packages from AWS Marketplace, see documentation
These example notebooks show you how to package a model or algorithm for listing in AWS Marketplace for machine learning.
- Creating Marketplace Products
- Creating a Model Package - Listing on AWS Marketplace provides a detailed walkthrough on how to package a pre-trained model as a SageMaker Model Package that can be listed on AWS Marketplace.
- Creating Algorithm and Model Package - Listing on AWS Marketplace provides a detailed walkthrough on how to package a scikit learn algorithm to create SageMaker Algorithm and SageMaker Model Package entities that can be used with the enhanced SageMaker Train/Transform/Hosting/Tuning APIs and listed on AWS Marketplace.
Once you have created an algorithm or a model package to be listed in the AWS Marketplace, the next step is to list it in AWS Marketplace, and provide a sample notebook that users can use to try your algorithm or model package.
- Curate your AWS Marketplace Model Package listing and sample notebook provides instructions on how to craft a sample notebook to be associated with your Model Package listing and how to curate a good AWS Marketplace listing that makes it easy for your customers to consume your Model Package.
- Curate your AWS Marketplace algorithm listing and sample notebook provides instructions on how to craft a sample notebook to be associated with your listing and how to curate a good AWS Marketplace listing that makes it easy for your customers to consume your algorithm.
These examples show you how to use model-packages and algorithms from AWS Marketplace for machine learning.
- Using Algorithms
- Using Algorithm From AWS Marketplace provides a detailed walkthrough on how to use Algorithm with the enhanced SageMaker Train/Transform/Hosting/Tuning APIs by choosing a canonical product listed on AWS Marketplace.
- Using AutoML algorithm provides a detailed walkthrough on how to use AutoML algorithm from AWS Marketplace.
- Using Implicit BPR Algorithm provides a detailed walkthrough on how to build a recommender system for implicit feedback datasets to train, evaluate and host your model to perform the batch and real-time inferences.
- Using Model Packages
- Using Model Packages From AWS Marketplace is a generic notebook which provides sample code snippets you can modify and use for performing inference on Model Packages from AWS Marketplace, using Amazon SageMaker.
- Using Amazon Demo product From AWS Marketplace provides a detailed walkthrough on how to use Model Package entities with the enhanced SageMaker Transform/Hosting APIs by choosing a canonical product listed on AWS Marketplace.
- Using models for extracting vehicle metadata provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of auto-insurance claim processing.
- Using models for identifying non-compliance at a workplace provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of generating summary reports for identifying non-compliance at a construction/industrial workplace.
- Creative writing using GPT-2 Text Generation will show you how to use AWS Marketplace GPT-2-XL pre-trained model on Amazon SageMaker to generate text based on your prompt to help you author prose and poetry.
- Amazon Augmented AI with AWS Marketplace ML models will show you how to use AWS Marketplace pre-trained ML models with Amazon Augmented AI to implement human-in-loop workflow reviews with your ML model predictions.
- Monitoring data quality in third-party models from AWS Marketplace will show you how to perform Data Quality monitoring on a pre-trained third-party model from AWS Marketplace.
- Evaluating ML models from AWS Marketplace for person counting use case will show you how to use two AWS Marketplace GluonCV pre-trained ML models for person counting use case and evaluate each model for performance in different types of crowd images.
- Preprocess audio data using a pre-trained machine learning model demonstrates the usage of a pre-trained audio track separation model to create synthetic features and improve an acoustic classification model.
- Using Dataset Products
- Using dataset from AWS Data Exchange with ML model from AWS Marketplace is a sample notebook which shows how a dataset from AWS Data Exchange can be used with an ML Model Package from AWS Marketplace.
- Using Shutterstock Image Datasets to train Image Classification Models provides a detailed walkthrough on how to use the Free Sample: Images & Metadata of “Whole Foods” Shoppers from Shutterstock's Image Datasets to train a multi-label image classification model using Shutterstock's pre-labeled image assets. You can learn more about this implementation from this blog post.
What do I need in order to get started?
- The quickest setup to run example notebooks includes:
- An AWS account
- Proper IAM User and Role setup
- An Amazon SageMaker Notebook Instance
- An S3 bucket
- AWS Marketplace Subscription to the algorithm/model you wish to use.
- AWS Data Exchange Subscription to the dataset product you wish to use.