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