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Building AI-Powered Applications on AWS: A Practical Guide (Part 1)
This article provides a comprehensive introduction to AWS AI services, particularly Amazon SageMaker, explaining how to set up the development environment and create machine learning models. It includ
es a practical example of building a classification model using the Iris dataset, demonstrating the basic workflow of machine learning development in SageMaker.

Reasons to Read -- Learn:

  • how to set up and configure Amazon SageMaker Studio, a fully managed service that simplifies the complex process of building and deploying machine learning models on AWS.
  • how to create and run Jupyter notebooks in SageMaker, including practical code examples using popular Python libraries like pandas and scikit-learn for machine learning development.
  • step-by-step process of training a basic classification model using the Iris dataset, which serves as a foundation for understanding machine learning workflows in AWS.
  • 4 min readauthor: xinacod
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