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AWS SageMaker

Hands-on, persona-driven learning with AWS SageMaker for real-world results.
Alistair Sutherland
AWS consultant and Instructor
DevOps Pre-Requisite Course
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What you’ll learn

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Description

Welcome to the AWS SageMaker course, where you'll learn utliize AWS's machine learning capabilities for building and hosting models efficiently. This course is designed for both AWS novices and AWS seasoned professionals, who are starting out on their journey to developing the ML skills to manage, deploy, and scale machine learning projects using SageMaker's features. Even if you have no ML or SageMaker experience, the course will jump start your ML learning. The course will follow a typical machine learning pipeline from data preparation all the way through to hosting and monitoring. Activities in the pipeline align to different personas and so the course follows the persona actions for each stage.

  • Pre-requisites: This section outlines the essential foundation needed for your SageMaker learning journey. You'll review machine learning basics, understand the necessary mathematical concepts, and learn why SageMaker may initially seem complex. The section also emphasizes the advantages of learning through persona-based actions, ensuring a targeted and effective educational experience.
  • SageMaker Introduction: Dive into SageMaker as a powerful managed service. This section introduces key personas—data engineers, scientists, and MLOps engineers—showcasing SageMaker’s versatility. Learn about Jupyter Notebooks, working locally first and then remotely hosted, and the benefits of the SageMaker SDK for Python over other tools. You'll also explore data preparation essentials for ensuring you have high-quality data that is ready for model training.
  • SageMaker UI: Mastering the SageMaker user interface is crucial. This section guides you through UI navigation, comparing legacy notebooks with SageMaker Studio, and exploring code editor alternatives. Understand the differences between SageMaker Studio Classic and the new version, enabling you to optimize your workflow and collaboration efforts.
  • Persona SageMaker Activities - Data Engineer: This section equips data engineers with tools for large-scale data preparation and management. Explore tabular data preparation, SageMaker Canvas, AutoML, and Jupyter Notebooks for data processing. Gain skills to streamline data workflows, ensuring efficient transformation of raw data into actionable insights.
  • Persona SageMaker Activities - Data Scientist: Tailored for data scientists, this section covers feature engineering, model training, and optimization in SageMaker Studio. Learn to manage experiments and track models using the SageMaker Model Registry. Practical activities ensure you're ready to enhance model accuracy and streamline deployment.
  • Persona SageMaker Activities - MLOps Engineer: MLOps engineers will learn to manage and deploy models effectively. Explore hosting options, advanced inference, and automating pipelines with SageMaker. Integration with tools like Apache Airflow and model monitoring strategies prepare you to manage scalable, reliable ML workflows in production.
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About the instructor

Alistair is a seasoned AWS consultant and instructor with over 20 years of experience in the IT industry. He has worked across a wide range of sectors, from startups to global enterprises, bringing a deep understanding of real-world infrastructure and cloud challenges.

For the past two years, Alistair has been focused on helping enterprise retail banks productionize their SageMaker platforms—working hands-on with data scientists and platform teams to build scalable, reliable ML solutions. This practical experience translates directly into the course, ensuring learners gain insights grounded in reality, not just theory.

Known for his clear, structured teaching style, Alistair excels at breaking down complex topics so they’re accessible to everyone, regardless of background.

He holds multiple AWS certifications, including:

  • AWS Certified DevOps Engineer – Professional
  • AWS Certified Solutions Architect – Professional
  • AWS Certified Machine Learning – Specialty
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Course Introduction

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Module Content

Course Overview 04:05
Important Note to Learners
How to Reach Out to KodeKloud and Engage with the Community

Machine Learning Prerequisites

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Course Introduction 27:58
Prerequisites 13:29
ML Basics - Fundamentals of Model Training and Inference 24:06
ML Basics - Fundamentals of Model Training and Inference - Part 2 15:20
ML Pipeline 37:10
How Much Math Do I Need? 30:40
How Much Math Do I Need? - Part 2 21:26
How Much Math Do I Need? - Part 3 16:27
Why is SageMaker Intimidating? 25:01
Why Learn SageMaker by Persona? 31:16
Why Learn SageMaker by Persona? - Part 2 19:57

SageMaker Introduction

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SageMaker - What Makes it a Managed Service? 24:55
Introduction to Jupyter Notebooks 20:33
Introduction to Jupyter Notebooks - Part 2 18:47
Demo: Jupyter Notebooks 22:10
Lab: Working with Jupyter Notebooks
The SageMaker SDK for Python - Why not boto3? 25:56

SageMaker User Interface

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Navigating the UI 18:58
Demo: Navigating the UI 18:51
SageMaker Domains and SageMaker Studio 21:38
SageMaker Domains and SageMaker Studio - Part 2 30:49
SageMaker Domains and SageMaker Studio - Part 3 23:44
Lab: Create SageMaker Domain and User Profile
Options for Collaboration in SageMaker Studio 25:15
Code Editor Alternative to JupyterLab 19:07
Code Editor Alternative to JupyterLab - Part 2 13:27
SageMaker Studio Classic vs SageMaker Studio New​ 22:22

Persona SageMaker Activities - Data Engineer

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Tabular Data Preparation 30:13
SageMaker Canvas Low Code Data Preparation and ML Training 25:55
SageMaker Canvas Low Code Data Preparation and ML Training - Part 2 22:14
Demo: SageMaker Canvas Data Wrangler 26:12
Demo: SageMaker Canvas Data Wrangler - Part 2 15:49
Demo: SageMaker Canvas AutoML 15:17
SageMaker Data Processing Jobs 29:47
Lab: Creating SageMaker Data Processing Jobs

Persona SageMaker Activities - Data Scientist

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An introduction to feature engineering 19:48
Demo: Feature Engineering in SageMaker Studio 33:40
Training Model in SageMaker Studio and Monitoring Training Jobs 14:27
Demo: Training Your Model in SageMaker Studio Using Python SDK 36:44
Lab: Training Your Model in SageMaker Studio Using Python SDK
Introduction to Experiment Management 25:44
Optimizing the Training Process with Automation 22:43
Keeping Track of Models Using the SageMaker Model Registry 12:16
Demo: Keeping Track of Models Using the SageMaker Model Registry 27:38

Persona SageMaker Activities - MLOps Engineer

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Options to Host a Model for Inference 14:46
Advanced Inference Options 11:57
Demo: Deploy a Hosted Model Using SageMaker Endpoints 16:16
Lab: Deploy a Hosted Model Using SageMaker Endpoints
Automate ML Pipeline with SageMaker Pipeline
End-to-End CI/CD ML Pipelines from SageMaker Project Templates 35:10
Demo: End-to-End CI/CD ML Pipelines from SageMaker Project Templates 38:01
Monitoring and Observability 20:03
Demo: Add Monitoring to Deployed Model 12:32

Wrap Up

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Demystifying Advanced SageMaker 20:53
Demystifying Advanced SageMaker - Part 2 19:12
Demystifying Advanced SageMaker - Part 3 14:19
What's New for 2025? 19:49
Is SageMaker Demystified for You? 20:27
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