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Practical Data Science with Amazon SageMaker is a hands-on course designed to equip data professionals with the practical skills needed to build, train, deploy, and manage machine learning models using Amazon SageMaker. The course covers the end-to-end ML lifecycle on AWS, from data preparation and feature engineering to model deployment and monitoring — all within the SageMaker environment. Participants will gain valuable experience with real-world datasets and use Amazon’s cloud-native tools to streamline data science workflows.
Prerequisites:
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Foundational knowledge of Python programming
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Basic understanding of machine learning concepts
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Familiarity with AWS services is beneficial but not mandatory
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Prior experience with Jupyter notebooks is helpful
What You Gain After the Course
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Practical experience using Amazon SageMaker to build ML solutions
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Understanding of the ML pipeline from data preparation to model deployment
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Confidence in evaluating and tuning machine learning models in a managed environment
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Ability to deploy scalable, secure ML applications on AWS infrastructure
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Skills to leverage SageMaker Studio and SageMaker Pipelines for collaborative ML workflows
Jobs You Can Get After Completing This Course:
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Machine Learning Engineer
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Data Scientist
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AI/ML Solutions Architect
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Cloud Data Engineer (specializing in AWS)
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Applied AI Engineer


Certification