Practical Data Science with Amazon SageMaker

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 Contents

Module 1: Introduction to machine learning
Module 2: Introduction to data prep and SageMaker
Module 3: Problem formulation and dataset
Module 4: Data analysis and visualization
Module 5: Training and evaluating a model
Module 6: Automatically tune a model
Module 7: Deployment / production readiness
Module 8: Relative cost of errors
Module 9: Amazon SageMaker architecture and featuresAccessing Amazon SageMaker notebooks in a VPC

You have access to the labs for another 14 days after the course. This way you can repeat exercises or deepen them individually.

You will receive the original course documentation by Amazon Web Services in English language as an e-book.

Target Group

This course is intended for:
Developers
Data Scientists

Knowledge Prerequisites

We recommend that attendees of this course have:
Familiarity with Python programming language
Basic understanding of Machine Learning

Important: Please bring your notebook to the course! If this is not possible, please contact us in advance.

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

Classroom training: Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
Online training: You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
Tailor-made courses: You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.

You can find the complete description of this course with dates and prices ready for download at as PDF.