Official AWS Course

Practical Data Science with Amazon SageMaker Training

Spend a day in the life of a data scientist: prepare data, train, tune, and deploy an ML model on Amazon SageMaker. 1-day official AWS course with hands-on labs, built for developers and DevOps engineers.

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Course Overview

This course is not designed to turn you into a data scientist. It is designed to let you work effectively alongside one.

Developers and operations engineers increasingly build applications that depend on machine learning models they did not create, and the handoffs go badly when neither side understands the other's constraints. Over one day you spend a day in the life of a data scientist, following the basic process they use to develop ML solutions on AWS with Amazon SageMaker, so that collaboration becomes practical rather than a negotiation.

You will start with why machine learning helps certain business problems and not others, how to frame a problem, what prediction quality actually means, and who does what on a team that builds and ships ML systems. From there you move through the lifecycle hands-on: preparing a dataset with SageMaker Data Wrangler, choosing an algorithm and training a model in SageMaker, evaluating and tuning it with hyperparameter optimization, and deploying it to a real-time endpoint to generate predictions.

The last third addresses what happens after the model works. Operational challenges cover responsible ML, how ML teams and MLOps function, automation, monitoring, and updating models safely. The course closes by mapping the wider toolset to different skill levels and needs, including no-code machine learning with Amazon SageMaker Canvas and experimentation in SageMaker Studio Lab, with an optional lab integrating a web application with a SageMaker model endpoint.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

Upcoming Schedules

Start Date	Time	Duration	Mode	Price
Sep 04, 2026	9:00 am - 5:00 pm	1 Day	online	$649	GTR
Sep 18, 2026	9:00 am - 5:00 pm	1 Day	online	$649	GTR
Oct 02, 2026	9:00 am - 5:00 pm	1 Day	online	$649	GTR
Oct 16, 2026	9:00 am - 5:00 pm	1 Day	online	$649	GTR
Oct 30, 2026	9:00 am - 5:00 pm	1 Day	online	$649	GTR

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What you will learn

Discuss the benefits of different types of machine learning for solving business problems
Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
Explain how data scientists use AWS tools and ML to solve a common business problem
Summarize the steps a data scientist takes to prepare data
Summarize the steps a data scientist takes to train ML models
Summarize the steps a data scientist takes to evaluate and tune ML models
Summarize the steps to deploy a model to an endpoint and generate predictions
Describe the challenges of operationalizing ML models
Match AWS tools with their ML function

Who Should Attend This Course?

DevOps engineers who support or deploy ML workloads
Application developers building software that integrates with ML models
Technical professionals who work alongside data science teams and need a working understanding of their process
Anyone preparing to take MLOps Engineering on AWS, which lists this course as a prerequisite

Prerequisites

AWS recommends attendees have:

AWS Technical Essentials, or equivalent AWS knowledge
Entry-level knowledge of Python programming
Entry-level knowledge of statistics

Entry-level is the operative phrase. You need enough Python to read and modify code in a notebook, and enough statistics to interpret what a model evaluation is telling you. Prior machine learning experience is not required.

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📘 Practical Data Science with Amazon SageMaker Outline

The benefits of machine learning
Types of ML approaches
Framing the business problem
Prediction quality
Processes, roles, and responsibilities on ML projects

Data analysis and preparation
Data preparation tools
Demonstration: Amazon SageMaker Studio and notebooks
Hands-on lab: data preparation with SageMaker Data Wrangler

The steps involved in training a model
Choosing an algorithm
Training the model in Amazon SageMaker
Hands-on lab: training a model with Amazon SageMaker
AI-assisted coding in SageMaker Studio notebooks, with demonstration

Model evaluation
Model tuning and hyperparameter optimization
Hands-on lab: model tuning and hyperparameter optimization with Amazon SageMaker

Model deployment
Hands-on lab: deploy a model to a real-time endpoint and generate a prediction

Responsible ML
The ML team and MLOps
Automation
Monitoring
Updating models, including testing and deployment

Matching tools to different skills and business needs
No-code ML with Amazon SageMaker Canvas, with demonstration
Amazon SageMaker Studio Lab, with demonstration
Optional hands-on lab: integrating a web application with a SageMaker model endpoint

❓ Frequently Asked Questions

No, and that is rather the point. AWS built this course for DevOps engineers and application developers who need to collaborate with data scientists and build applications that integrate with ML. You come away understanding the process and able to have informed conversations about it, not able to design novel models.

For what this course sets out to do, yes. It is a guided walk through one complete lifecycle rather than a deep dive into any part of it. You will prepare data, train a model, tune it, and deploy it to an endpoint in a single day, with four hands-on labs. The depth comes later, from the courses this one leads into.

Enough that you need entry-level Python. The labs run in SageMaker Studio notebooks, and you will read and modify code rather than write applications from scratch. If Python is completely unfamiliar, address that before attending.

It depends on direction. If you are heading toward operationalizing models, Machine Learning Engineering on AWS is the natural next step. Experienced data scientists who want to work efficiently inside SageMaker Studio should look at Amazon SageMaker Studio for Data Scientists instead, which is an advanced course.

Canvas is AWS's no-code machine learning tool. It is included because not every ML problem in an organization needs a data scientist and a notebook, and knowing when a no-code approach is sufficient is a genuinely useful judgment. Module 7 also covers SageMaker Studio Lab for lightweight experimentation.

Yes, and it works particularly well for mixed groups. Sending developers, operations staff, and data scientists together tends to fix the handoff problems the course is designed to address.

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