# Listings for your own algorithms and models with the AWS Marketplace
Selling Amazon SageMaker AI algorithms and model packages is a three-step process:
1. Develop your algorithm or model, and package it in a Docker container. For information, see [Develop Algorithms and Models in Amazon SageMaker AI](sagemaker-marketplace-develop.md).
1. Create an algorithm or model package resource in SageMaker AI. For information, see [Creation of Algorithm and Model Package Resources](sagemaker-mkt-create.md).
1. Register as a seller on AWS Marketplace and list your algorithm or model package on AWS Marketplace. For information about registering as a seller, see [Getting Started as a Seller](https://docs.aws.amazon.com/marketplace/latest/userguide/user-guide-for-sellers.html) in the *User Guide for AWS Marketplace Providers*. For information about listing and monetizing your algorithms and model packages, see [Listing Algorithms and Model Packages in AWS Marketplace for Machine Learning](https://docs.aws.amazon.com/marketplace/latest/userguide/listing-algorithms-and-model-packages-in-aws-marketplace-for-machine-learning.html) in the *User Guide for AWS Marketplace Providers*.
![\[The seller's workflow in SageMaker AI.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/seller-flow.png)
## Topics
+ [Develop Algorithms and Models in Amazon SageMaker AI](sagemaker-marketplace-develop.md)
+ [Creation of Algorithm and Model Package Resources](sagemaker-mkt-create.md)
+ [List Your Algorithm or Model Package on AWS Marketplace](sagemaker-mkt-list.md)
# Develop Algorithms and Models in Amazon SageMaker AI
Before you can create algorithm and model package resources to use in Amazon SageMaker AI or list on AWS Marketplace, you have to develop them and package them in Docker containers.
**Note**
When algorithms and model packages are created for listing on AWS Marketplace, SageMaker AI scans the containers for security vulnerabilities on supported operating systems.
Only the following operating system versions are supported:
Debian: 6.0, 7, 8, 9, 10
Ubuntu: 12.04, 12.10, 13.04, 14.04, 14.10, 15.04, 15.10, 16.04, 16.10, 17.04, 17.10, 18.04, 18.10
CentOS: 5, 6, 7
Oracle Linux: 5, 6, 7
Alpine: 3.3, 3.4, 3.5
Amazon Linux
**Topics**
+ [Develop Algorithms in SageMaker AI](#sagmeaker-mkt-develop-algo)
+ [Develop Models in SageMaker AI](#sagemaker-mkt-develop-model)
## Develop Algorithms in SageMaker AI
An algorithm should be packaged as a docker container and stored in Amazon ECR to use it in SageMaker AI. The Docker container contains the training code used to run training jobs and, optionally, the inference code used to get inferences from models trained by using the algorithm.
For information about developing algorithms in SageMaker AI and packaging them as containers, see [Docker containers for training and deploying models](docker-containers.md). For a complete example of how to create an algorithm container, see the sample notebook at [https://sagemaker-examples.readthedocs.io/en/latest/advanced\$1functionality/scikit\$1bring\$1your\$1own/scikit\$1bring\$1your\$1own.html](https://sagemaker-examples.readthedocs.io/en/latest/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.html). You can also find the sample notebook in a SageMaker notebook instance. The notebook is in the **Advanced Functionality** section, and is named `scikit_bring_your_own.ipynb`.
Always thoroughly test your algorithms before you create algorithm resources to publish on AWS Marketplace.
**Note**
When a buyer subscribes to your containerized product, the Docker containers run in an isolated (internet-free) environment. When you create your containers, do not rely on making outgoing calls over the internet. Calls to AWS services are also not allowed.
## Develop Models in SageMaker AI
A deployable model in SageMaker AI consists of inference code, model artifacts, an IAM role that is used to access resources, and other information required to deploy the model in SageMaker AI. Model artifacts are the results of training a model by using a machine learning algorithm. The inference code must be packaged in a Docker container and stored in Amazon ECR. You can either package the model artifacts in the same container as the inference code, or store them in Amazon S3.
You create a model by running a training job in SageMaker AI, or by training a machine learning algorithm outside of SageMaker AI. If you run a training job in SageMaker AI, the resulting model artifacts are available in the `ModelArtifacts` field in the response to a call to the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) operation. For information about how to develop a SageMaker AI model container, see [Containers with custom inference code](your-algorithms-inference-main.md). For a complete example of how to create a model container from a model trained outside of SageMaker AI, see the sample notebook at [https://sagemaker-examples.readthedocs.io/en/latest/advanced\$1functionality/xgboost\$1bring\$1your\$1own\$1model/xgboost\$1bring\$1your\$1own\$1model.html](https://sagemaker-examples.readthedocs.io/en/latest/advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.html).
Always thoroughly test your models before you create model packages to publish on AWS Marketplace.
**Note**
When a buyer subscribes to your containerized product, the Docker containers run in an isolated (internet-free) environment. When you create your containers, do not rely on making outgoing calls over the internet. Calls to AWS services are also not allowed.
# List Your Algorithm or Model Package on AWS Marketplace
After creating and validating your algorithm or model in Amazon SageMaker AI, list your product on AWS Marketplace. The listing process makes your products available in the AWS Marketplace and the SageMaker AI console.
To list products on AWS Marketplace, you must be a registered seller. To register, use the self-registration process from the AWS Marketplace Management Portal (AMMP). For information, see [Getting Started as a Seller](https://docs.aws.amazon.com/marketplace/latest/userguide/user-guide-for-sellers.html) in the *User Guide for AWS Marketplace Providers*. When you start the product listing process from the Amazon SageMaker AI console, we check your seller registration status. If you have not registered, we direct you to do so.
To start the listing process, do one of the following:
+ From the SageMaker AI console, choose the product, choose **Actions**, and choose **Publish new ML Marketplace listing**. This carries over your product reference, the Amazon Resource Name (ARN), and directs you to the AMMP to create the listing.
+ Go to [ML listing process](https://aws.amazon.com/marketplace/management/ml-products/), manually enter the Amazon Resource Name (ARN), and start your product listing. This process carries over the product metadata that you entered when creating the product in SageMaker AI. For an algorithm listing, the information includes the supported instance types and hyperparameters. In addition, you can enter a product description, promotional information, and support information as you would with other AWS Marketplace products.