Practical Data Science with Amazon SageMaker Training

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In this intermediate-level Practical Data Science with Amazon SageMaker Training course, individuals learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This Practical Data Science with Amazon SageMaker 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.

Practical Data Science with Amazon SageMaker TrainingDuration: 1 day

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  • We can adapt this Practical Data Science with Amazon SageMaker course to your group’s background and work requirements at little to no added cost.
  • If you are familiar with some aspects of this Practical Data Science with Amazon SageMaker course, we can omit or shorten their discussion.
  • We can adjust the emphasis placed on the various topics or build the Practical Data Science with Amazon SageMaker Course around the mix of technologies of interest to you (including technologies other than those included in this outline).
  • If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the Practical Data Science with Amazon SageMaker course in manner understandable to lay audiences.
AUDIENCE/TARGET GROUP

The target audience for this Practical Data Science with Amazon SageMaker course:

  • Developers
  • Data Scientists
CLASS PREREQUISITES

The knowledge and skills that a learner must have before attending this Practical Data Science with Amazon SageMaker course are:

  • Familiarity with Python programming language
  • Basic understanding of Machine Learning
Practical Data Science with Amazon SageMaker Training - OBJECTIVES

Upon completing this Practical Data Science with Amazon SageMaker course, learners will be able to meet these 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
Practical Data Science with Amazon SageMaker Training - COURSE SYLLABUS

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
Practical Data Science with Amazon SageMaker Training Course Wrap-Up

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