Practical Data Science with Amazon SageMaker

ONLINE TRAINING
February 23 2021
April 21 2021
December 14 2021
CLASSROOM TRAINING
October 4 2021
Training Cost
ONLINE TRAINING
545 EUR (VAT ex.) per person
CLASSROOM TRAINING
595 EUR (VAT ex.) per person
Practical informationClass from 9 AM to 5 PM
LanguageEnglish (unless all attendees speak Dutch)
Location

Most of our classroom training courses take placeĀ in Belgium (Edegem) or The Netherlands (Breda). Please click the button with the desiredĀ date to check the exact location of the training.

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Duration

1 day

Course overview

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.

Who should attend this training

This course is intended for:

  • Developers

  • Data Scientists

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 participants to have the following prerequisites:

  • Familiarity with Python programming language

  • Basic understanding of Machine Learning

Course Content

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

  • 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

ENROLL NOW
This training in-company?
Upon your request we can organize this training for you.
CONTACT US