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    Practical Data Science with Amazon SageMaker (PDSASM)

    Cloud Computing - AWS
    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.

    Practical Data Science with Amazon SageMaker (PDSASM) 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

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    Key Point of Training Programs

    • Practical Data Science with Amazon SageMaker (PDSASM) Prerequisites

      Who should attend
      This course is intended for:

      Development Operations (DevOps) engineers
      Application developers
      Certifications
      This course is part of the following Certifications:

      AWS Certified Machine Learning – Specialty
      Prerequisites
      We recommend that attendees of this course have:

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

    • Practical Data Science with Amazon SageMaker (PDSASM) Delivery Format

      In-Person

      Online

    • Practical Data Science with Amazon SageMaker (PDSASM) 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