Practical Data Science with Amazon SageMaker

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Course ID: AWSPDS
Duration: 1 Day
Training Fee: HK$6,000
Private in-house training
Apart from public, instructor-led classes, we also offer private in-house trainings for organizations based on their needs. Call us at +852 2116 3328 or email us at [email protected] for more details.

Why Choose Us?

  • The First Authorised Training Partner of AWS with full license
  • The Most Training Schedules delivered by AWS Authorized Instructors (AAI) and AAI Champion
  • Best Price Guaranteed
  • Trained over 50,000 talents in Asia
  • High Passing Rate: 90%
  • Appointed Exam Centre
Course Objectives

In this course, you will learn how to:

  • Prepare a dataset for training
  • Train and evaluate a Machine Learning model
  • Automatically tune a Machine Learning model
  • Prepare a Machine Learning model for production
  • Think critically about Machine Learning model results
Prerequisites

We recommend the following prerequisites for attendees of this course:

  • Familiarity with Python programming language
  • Basic understanding of Machine Learning
Intended Audience

This course is intended for:

  • Developers
  • Data Scientists
Delivery Method

This course will be delivered through a mix of:

  • Instructor-Led Training (ILT)
  • Hands-On Labs
  • Group exercises
Course Outline

This course will cover the following concepts:

 

Module 1: Introduction to machine learning

  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline

 

Module 2: Introduction to data prep and SageMaker

  • Training and test dataset defined
  • Introduction to SageMaker
  • Demonstration: SageMaker console
  • Demonstration: Launching a Jupyter notebook

 

Module 3: Problem formulation and dataset preparation

  • Business challenge: Customer churn
  • Review customer churn dataset

 

Module 4: Data analysis and visualization

  • Demonstration: Loading and visualizing your dataset
  • Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demonstration: Cleaning the data

 

Module 5: Training and evaluating a model

  • Types of algorithms
  • XGBoost and SageMaker
  • Demonstration: Training the data
  • Exercise 3: Finishing the estimator definition
  • Exercise 4: Setting hyper parameters
  • Exercise 5: Deploying the model
  • Demonstration: hyper parameter tuning with SageMaker
  • Demonstration: Evaluating model performance

 

Module 6: Automatically tune a model

  • Automatic hyper parameter tuning with SageMaker
  • Exercises 6-9: Tuning jobs

 

Module 7: Deployment / production readiness

  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling
  • Demonstration: Configure and test auto scaling
  • Demonstration: Check hyper parameter tuning job
  • Demonstration: AWS Auto Scaling
  • Exercise 10-11: Set up AWS Auto Scaling

 

Module 8: Relative cost of errors

  • Cost of various error types
  • Demo: Binary classification cutoff

 

Module 9: Amazon SageMaker architecture and features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

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