As machine learning becomes increasingly prevalent in a wide range of industries, organizations are finding the need to train and serve large numbers of machine learning models to meet the diverse needs of their customers. For SaaS providers in particular, the ability to train and serve thousands of models efficiently and cost-effectively is crucial for staying competitive in a rapidly evolving market.
Training and serving thousands of models requires a robust and scalable infrastructure, and this is where Amazon SageMaker (http://aws.amazon.com/sagemaker) can help. Amazon SageMaker is a fully-managed platform that enables developers and data scientists to build, train, and deploy machine learning models quickly, while also offering the cost-saving benefits of using Amazon's cloud infrastructure.
In this notebook, we will explore how SageMaker's features, including SageMaker Processing, SageMaker Training Jobs, and Amazon SageMaker Multi-Model Endpoint, can be used to train and serve thousands of models in a cost-effective way.
Run the accompanying notebook.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.