Learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker.
This course walks through the stages of a typical data science process for Machine Learning from analysing and visualising 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.
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What you’ll learn
This course is designed to teach participants 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
AWS at Lumify Work
Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.
Stay ahead of the technology curve
Don’t let your tech outpace the skills of your people
Train Anywhere
From our state-of-the-art classrooms to telepresence to your offices, our instructor-led training caters to your needs.
Track Record
We have a 30-year history of driving innovative, award-winning learning solutions.
More Courses, More Often
When you train with Lumify Work you get more courses, more often, in more locations, and from more vendors.
Quality Instructors and Content
Expert instructors with real world experience and the latest vendor-approved in-depth course content.
Partner-Preferred Supplier
Chosen and awarded by the world's leading vendors as preferred training partner.
Ahead of the Technology Curve
No matter your chosen technologies or platforms, we can help you stay one step ahead.
Train Anywhere
From our state-of-the-art classrooms to telepresence to your offices, our instructor-led training caters to your needs.
Track Record
We have a 30-year history of driving innovative, award-winning learning solutions.
More Courses, More Often
When you train with Lumify Work you get more courses, more often, in more locations, and from more vendors.
Quality Instructors and Content
Expert instructors with real world experience and the latest vendor-approved in-depth course content.
Partner-Preferred Supplier
Chosen and awarded by the world's leading vendors as preferred training partner.
Ahead of the Technology Curve
No matter your chosen technologies or platforms, we can help you stay one step ahead.
Train Anywhere
From our state-of-the-art classrooms to telepresence to your offices, our instructor-led training caters to your needs.
Track Record
We have a 30-year history of driving innovative, award-winning learning solutions.
More Courses, More Often
When you train with Lumify Work you get more courses, more often, in more locations, and from more vendors.
Who is the course for?
This course is intended for:
Developers
Data scientists
We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please contact us on 1800 U LEARN (1800 853 276)
Course subjects
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 visualisation
Demonstration: Loading and visualising 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
Please note: This is an emerging technology course. Course outline is subject to change as needed.
Prerequisites
It is recommended that attendees have the following prerequisites:
Familiarity with Python programming language
Basic understanding of Machine Learning
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Terms & Conditions
The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.
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