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The Complete Guide to Machine Learning on AWS with Amazon SageMaker
- AWS SageMaker is a service for building, training, and deploying machine learning models on AWS.
- SageMaker simplifies the machine learning workflow and provides tools and infrastructure for building, training, and deploying models.
- SageMaker is part of the AWS ecosystem, and works in sync with other AWS solutions to unify your machine learning workflow.
- You need to create an AWS account to use SageMaker.
- The article covers end-to-end workflow which involves a different set of stages such as exploratory data analysis, data engineering, data preprocessing, training, evaluation and deployment.
- AWS SageMaker allows you to train models on low cost, low configuration machines for small datasets.
- SageMaker offers Spot Training instances, enabling users to get high-powered computing resources at a lower cost.
- You can deploy the ML model using "Create endpoint" option in SageMaker dashboard or using the .deploy() method in the notebook instance.
- Once the model is deployed, you can use the .predict() method of the endpoint object to generate predictions for a classification task.
- The article concludes by recommending exploring the example notebooks and official documentation for SageMaker Python SDK.
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