Amazon SageMaker is a fully-managed machine learning service that enables developers and data scientists to build, train, and deploy machine learning models at scale. Amazon SageMaker removes the overhead and complexity traditionally associated with building, training, and deploying machine learning models, so developers and data scientists can focus on the areas that differentiate their business.
Amazon SageMaker provides a complete machine learning workflow, from data pre-processing and model development to deployment and inference. With just a few clicks in the Amazon SageMaker console, developers and data scientists can build and train machine learning models, and then deploy those models to perform predictions on new data. Amazon SageMaker takes care of all the heavy lifting required to build, train, and deploy machine learning models, so developers and data scientists can focus on the areas that differentiate their business. Amazon SageMer provides the following features:
-Data Pre-Processing: Amazon SageMaker provides built-in algorithms that can be used for data pre-processing, such as identifying outliers, imputing missing values, and performing Feature Engineering.
-Model Development: Amazon SageMaker provides built-in algorithms that can be used for model development, such asLinear Learner, Factorization Machines, and image classification.
-Model Deployment: Amazon SageMaker provides a fully-managed, scalable, and secure infrastructure for deploying machine learning models.
-Inference: Amazon SageMaker provides an easy-to-use prediction API that can be used to get predictions from deployed models.
