![]() ![]() It provides more than 15 widely used ML Algorithm for training purpose.The following diagram shows how machine learning works with AWS SageMaker. Now, let’s have a look at the concept of Machine Learning With AWS SageMaker and understand how to build, test, tune, and deploy a model. It is highly scalable and trains model faster.It helps in storing all ML components in one place. ![]() It reduces the time required for data labeling tasks.It reduces the cost of building machine learning models up to 70%.It inspects raw data and automatically creates, deploys, and trains model with complete visibility.It helps in creating and managing compute instance with the least amount of time.It enhances the productivity of a machine learning project.Some of the advantages of SageMaker are below: The AWS SageMaker comes with a pool of advantages (know all about it in the next section) Advantages of AWS SageMaker Amazon SageMaker is a cloud-based machine-learning platform that helps users create, design, train, tune, and deploy machine-learning models in a production-ready hosted environment.
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