Course Overview:

This course covers the following topics:

Introduction to Amazon SageMaker Studio: An overview of SageMaker Studio’s features, including its integrated Jupyter notebooks, model debugging, and experimentation tools.

Data Preprocessing: Techniques for preparing and cleaning datasets for training and inference.

Model Building and Training: How to build and train machine learning models using SageMaker’s built-in algorithms and AutoML capabilities.

Model Deployment and Monitoring: Deploying models to SageMaker endpoints and monitoring their performance.

Continuous Integration/Continuous Deployment (CI/CD) Pipelines: Automating the process of deploying and updating models in production.

After completing this course, students will be able to:

  • Use Amazon SageMaker Studio IDE to develop, train, and deploy machine learning models.
  • Prepare and clean datasets for training and inference.
  • Build and train models with SageMaker's algorithms and AutoML capabilities.
  • Deploy models to SageMaker endpoints and monitor performance.
  • Automate model deployment with CI/CD pipelines.
  • Debug and experiment with models for improved performance.

Upcoming Batches

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Key Features:

  • Hands-on labs for real-world application.

  • A comprehensive curriculum that covers all aspects of using SageMaker Studio.

  • Instruction from expert instructors.

  • A flexible schedule with online and in-person options.

  • A certification upon completion.

Who Should Attend:

  • Data scientists and machine learning practitioners
  • Professionals interested in Amazon SageMaker and AWS tools
  • Individuals looking to enhance their data science skills.

Prerequisites:

  • AWS Technical Essentials (1–day AWS instructor led course)

    • We recommend students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course:

  • The Machine Learning Pipeline on AWS (4–day AWS instructor led course)
  • Deep Learning on AWS (1–day AWS instructor led course)

    • Learning objective of the course:

    • Understand the key concepts of data science and machine learning.
    • Use Amazon SageMaker Studio for data exploration, model development, and model deployment.
    • Train and evaluate machine learning models using SageMaker algorithms and AutoML.
    • Deploy machine learning models to SageMaker endpoints for inference.
    • Monitor model performance using SageMaker's built-in monitoring tools.
    • Set up CI/CD pipelines to automate model deployment and updating.
    • Debug and experiment with models for improved performance.

    Course Outline: Download Course Outline

    • JupyterLab Extensions in SageMaker Studio
    • Demonstration: SageMaker user interface demo

    • Using SageMaker Data Wrangler for data processing
    • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
    • Using Amazon EMR
    • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
    • Using AWS Glue interactive sessions
    • Using SageMaker Processing with custom scripts
    • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
    • SageMaker Feature Store
    • Hands-On Lab: Feature engineering using SageMaker Feature Store

    • SageMaker training jobs
    • Built-in algorithms
    • Bring your own script
    • Bring your own container
    • SageMaker Experiments
    • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models

    • SageMaker Debugger
    • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
    • Automatic model tuning
    • SageMaker Autopilot: Automated ML
    • Demonstration: SageMaker Autopilot
    • Bias detection
    • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
    • SageMaker Jumpstart

    • SageMaker Model Registry
    • SageMaker Pipelines
    • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
    • SageMaker model inference options
    • Amazon SageMaker Studio for Data Scientists
    • Testing strategies, performance, and optimization
    • Hands-On Lab: Inferencing with SageMaker Studio

    • Amazon SageMaker Model Monitor
    • Discussion: Case study
    • Demonstration: Model Monitoring

    • Accrued cost and shutting down
    • Updates
    • Capstone
    • Environment setup
    • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
    • Challenge 2: Create feature groups in SageMaker Feature Store
    • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
    • Challenge 4: Use SageMaker Debugger for training performance and model optimization
    • Challenge 5: Evaluate the model for bias using SageMaker Clarify
    • Challenge 6: Perform batch predictions using model endpoint
    • Challenge 7: Automate full model development process using SageMaker Pipeline

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    Course ID: 19949

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