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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

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