Practical Data Science with Amazon SageMaker

Price
$675.00

Duration
1 Day

 

Delivery Methods
Virtual Instructor Led
Private Group

Course Overview

You will 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 case includes customer retention analysis to inform customer loyalty programs.

Course 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
  • Who Should Attend?

    Developers and Data Scientists.

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    Course Prerequisites

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

    1 - Introduction to Machine Learning

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

    2 - Introduction to Data Prep and SageMaker

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

    3 - Problem formulation and Dataset Preparation

    • Business Challenge: Customer churn
    • Review Customer churn dataset

    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

    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

    6 - Automatically Tune a Model

    • Automatic hyperparameter tuning with SageMaker
    • Exercises 6-9: Tuning Jobs

    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

    8 - Relative Cost of Errors

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

    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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