Practical Data Science with Amazon SageMaker Course

Last Updated: 27 09 2025

The Practical Data Science with Amazon SageMaker course is as hands-on as it is informative, so participants will learn much more than the core software. This course deals with a fully managed service, provided by AWS, called Amazon SageMaker, that abstracts away significant complexity (and time!) for every stage of the machine learning lifecycle. This free course is suitable for beginners eager to learn about machine learning as well as experienced data science practitioners. Learners will begin by learning core concepts, including types of machine learning, what job roles are typical in data science, and details of the ML pipeline.

Participants will work on launching Jupyter notebooks, preparing datasets, performing data analysis, and visualizing data to glean actionable insights. Other key topics include training and evaluation of models as well as hyperparameter tuning—all executed in the SageMaker environment. Advanced concepts such as model deployment, evaluation of production readiness, prediction error cost analysis (ensuring understanding of real-world ML workflows) are also explored so that learners gain a thorough grasp.

By the end of the course, Learners will efficiently leverage Amazon SageMaker in an accelerating manner for their data science projects which ensures a certain level of informed decision-making. This course is tailored as per professional’s requirements by SSDN Technologies, awarded as leading best corporate training company for expert-led programs with industry focused training content because they know how to train experts on world-class ML strategies and applied data science at the enterprise level. 

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Learning Options for You

  • Live Training (Duration : 8 Hours)
  • Per Participant

Fee: On Request

Course Prerequisites

  • Basic knowledge of Python programming and machine learning concepts
  • Familiarity with AWS services (S3, IAM, EC2) recommended
  • Understanding of data science workflows helpful

Learning Objectives

The Practical Data Science with Amazon SageMaker course provides hands-on experience in developing, training, and deploying ML models using AWS SageMaker. Participants learn to prepare and process data, select and train algorithms, tune hyperparameters, evaluate models, and deploy scalable endpoints. The course also covers integrating SageMaker with other AWS services for data pipelines, monitoring, and automation. Real-world case studies and labs demonstrate how SageMaker simplifies the ML lifecycle and accelerates time-to-value for AI solutions. Completing this course equips learners with practical skills to operationalize machine learning projects in the cloud.

Target Audience

  • Data scientists and machine learning engineers
  • Developers building AI/ML-powered applications
  • IT professionals managing ML workflows on AWS
  • Learners preparing for AWS Machine Learning certification

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