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.

Array

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

Prerequisites:

Prerequisites:

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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