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.

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. For a complete example of how to create an algorithm container, see the sample notebook at 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. For information about using the sample notebooks in a notebook instance, see Access example notebooks.

Always thoroughly test your algorithms before you create algorithm resources to publish on AWS Marketplace.

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 operation. For information about how to develop a SageMaker AI model container, see Containers with custom inference code. 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_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.html. You can also find the sample notebook in a SageMaker notebook instance. The notebook is in the Advanced Functionality section, and is named xgboost_bring_your_own_model.ipynb. For information about using the sample notebooks in a notebook instance, see Access example notebooks.

Always thoroughly test your models before you create model packages to publish on AWS Marketplace.