Amazon SageMaker

Amazon SageMaker is a fully managed machine learning service that helps developers and data scientists quickly and cost-effectively build, train, and deploy high-quality machine learning models. It provides a complete development environment, including visual interface, Jupyter notebook, automatic machine learning, model training and deployment and other functions. Users can build end-to-end machine learning solutions through SageMaker without managing any infrastructure.

Demand group:

["Rapidly develop prototypes or proof-of-concepts", "Train and deploy high-quality machine learning models", "Build an end-to-end machine learning pipeline", "Enable collaboration and DevOps across multiple stages"]

Example of usage scenario:

Use SageMaker Notebook to quickly develop an image classification model

Develop experiments with SageMaker Experiments organizational models

Use SageMaker Pipelines to build end-to-end MLOps workflows

Product features:

Provide flexible ML infrastructure such as Notebook instances and training instances

Built-in commonly used Jupyter Notebook and various machine learning frameworks

Support automatic feature engineering, model training, evaluation, and tuning

One-click model deployment, real-time or batch prediction

Complete model management and monitoring functions