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Practical Data Science with Sage Maker (PDSASM)

Learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker.
AWS Training Partner
Private session

This training is available in a private or personalized format. It can be provided in one of our training centres or at your offices. Call one of our consultants of submit a request online.

Call now at 1 877 624.2344

  • Duration: 1 day
  • Regular price: On request

Course outline

Reference : @AWS (PDSASM)

Duration : 1

In this intermediate-level course, individuals learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use cases include customer retention analysis to inform customer loyalty programs.

Audience

  • Developers
  • Data Scientists

Prerequisites

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

Objectives

  • 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

Contents

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
  • Demo: SageMaker console
  • Demo: 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
  • Demo: Loading and Visualizing your dataset
  • Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demo: Cleaning the data
Module 5: Training and Evaluating a Model
  • Types of Algorithms
  • XGBoost and SageMaker
  • Demo 5: Training the data
  • Exercise 3: Finishing the Estimator definition
  • Exercise 4: Setting hyperparameters
  • Exercise 5: Deploying the model
  • Demo: Hyperparameter tuning with SageMaker
  • Demo: Evaluating Model Performance
Module 6: Automatically Tune a Model
  • Automatic hyperparameter 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 Scaling
  • Demo: Configure and Test Autoscaling
  • Demo: Check Hyperparameter tuning job
  • Demo: AWS Autoscaling
  • Exercise 10-11: Set up AWS Autoscaling
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