Practical Data Science with Amazon SageMaker (AWS-PDSASM)

Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate with ML. You will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. You will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.

Course Information

Price: $695.00
Duration: 1 day
Learning Credits:
Course Delivery Options

Check out our full list of training locations and learning formats. Please note that the location you choose may be an Established HD-ILT location with a virtual live instructor.

Train face-to-face with the live instructor.

Access to on-demand training content anytime, anywhere.

Attend the live class from the comfort of your home or office.

Interact with a live, remote instructor from a specialized, HD-equipped classroom near you. An SLI sales rep will confirm location availability prior to registration confirmation.

All Sunset Learning dates are guaranteed to run!



We recommend that attendees of this course have:

  • AWS Technical Essentials
  • Entry-level knowledge of Python programming
  • Entry-level knowledge of statistics


Target Audience:

This course is intended for:

  • Development Operations (DevOps) engineers
  • Application developers


Course Objectives:

In this course, you will learn to:

  • Discuss the benefits of different types of machine learning for solving business problems
  • Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
  • Explain how data scientists use AWS tools and ML to solve a common business problem
  • Summarize the steps a data scientist takes to prepare data
  • Summarize the steps a data scientist takes to train ML models
  • Summarize the steps a data scientist takes to evaluate and tune ML models
  • Summarize the steps to deploy a model to an endpoint and generate predictions
  • Describe the challenges for operationalizing ML models
  • Match AWS tools with their ML function


Course Outline:

Module 1: Introduction to Machine Learning

  • Benefits of machine learning (ML)
  • Types of ML approaches
  • Framing the business problem
  • Prediction quality
  • Processes, roles, and responsibilities for ML projects

Module 2: Preparing a Dataset

  • Data analysis and preparation
  • Data preparation tools
  • Demonstration: Review Amazon SageMaker Studio and Notebooks
  • Hands-On Lab: Data Preparation with SageMaker Data Wrangler

Module 3: Training a Model

  • Steps to train a model
  • Choose an algorithm
  • Train the model in Amazon SageMaker
  • Hands-On Lab: Training a Model with Amazon SageMaker
  • Amazon CodeWhisperer
  • Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks

Module 4: Evaluating and Tuning a Model

  • Model evaluation
  • Model tuning and hyperparameter optimization
  • Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker

Module 5: Deploying a Model

  • Model deployment
  • Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction

Module 6: Operational Challenges

  • Responsible ML
  • ML team and MLOps
  • Automation
  • Monitoring
  • Updating models (model testing and deployment)

Module 7: Other Model-Building Tools

  • Different tools for different skills and business needs
  • No-code ML with Amazon SageMaker Canvas
  • Demonstration: Overview of Amazon SageMaker Canvas
  • Amazon SageMaker Studio Lab
  • Demonstration: Overview of SageMaker Studio Lab
  • (Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint


Looking to train an entire team? Check out our budget-friendly AWS Team Training Bundles for teams starting at just 5 people.