It’s not a course, it’s all the best courses in one

What you will learn

A comprehensive understanding of Amazon SageMaker and its capabilities

How to use Amazon SageMaker to prepare, train, deploy, and maintain machine learning models.

Gain hands-on experience using Amazon SageMaker through real-world use cases and projects.

Work with Amazon SageMaker in various industry verticals.


Our courses bring together the best resources from leading universities, companies, entrepreneurs and academics around the world to deliver a truly unparalleled learning experience.

Don’t waste your money, our team of expert curators offers carefully curated education, providing the highest quality educational resources from the most respected institutions and industry leaders to create the ultimate Amazon SageMaker course, an opportunity to acquire the best knowledge and skills in the field.

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Why not using videos? Why not using videos?  Text-based online courses are more effective than video courses because they allow learners to learn at their own pace, pause and reread sections they may have missed, and search for specific information more easily. Text also allows learners to take notes and review the material more easily.

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This course is designed to provide a comprehensive introduction to Amazon SageMaker, a fully-managed platform for building, deploying, and maintaining machine learning models. The course is aimed at developers, data scientists, and anyone interested in learning how to use Amazon SageMaker to solve real-world problems.

The course will cover the following topics:

  1. Introduction to Amazon SageMaker: This section will provide an overview of the platform and its capabilities.
  2. Building and Training Machine Learning Models: This section will cover the process of building and training machine learning models using Amazon SageMaker, including how to use popular frameworks such as TensorFlow and PyTorch.
  3. Deploying and Managing Models: This section will cover how to deploy and manage machine learning models using Amazon SageMaker, including how to create endpoints and monitor performance.
  4. Real-World Use Cases: This section will provide students with hands-on experience working with real-world use cases and problems in various industry verticals such as healthcare, retail and finance.

Throughout the course, students will have the opportunity to work on projects and exercises to gain hands-on experience with Amazon SageMaker.

By the end of the course, students will have a strong understanding of how to use Amazon SageMaker to build, deploy, and manage machine learning models, and will be able to apply their knowledge to solve real-world problems in various industries.

Don’t miss this opportunity to stay one step ahead and master the skills you need. Enroll in the course today and start your journey to becoming a leading expert in your field.




Let´s Start!

Introduction to Amazon SageMaker: Overview of the service, use cases, and benefi

What is Amazon SageMaker and how it differs from other machine learning services
Use cases and benefits of using Amazon SageMaker.

Getting Started with Amazon SageMaker: Setting up an account, creating an endpoi

Setting up an Amazon SageMaker account and creating a development endpoint.
Creating and deploying a simple machine learning model using Amazon SageMaker.
Understanding the different components of the Amazon SageMaker service: Notebook

Data Preparation: Importing and cleaning data, creating datasets, and Jupyter

Importing data using various methods such as S3, Kinesis, and IoT.
Cleaning and preprocessing data using built-in libraries and custom code.
Creating datasets and working with Jupyter notebooks in Amazon SageMaker.

Model Training: Creating, customizing, training and tuning models

Understanding the different types of models available in Amazon SageMaker.
Creating and customizing models using built-in libraries and custom code.
Training and tuning models using Amazon SageMaker’s built-in tools.
Evaluating model performance using various metrics.

Model Deployment: Deploying, monitoring and maintaining. A/B testing.

Deploying models to production and creating endpoints.
Monitoring and maintaining deployed models with SageMaker’s built-in tools
A/B testing models to compare performance.

Advanced Topics: Reinforcement learning, built-in algorithms, custom container.

Working with reinforcement learning in Amazon SageMaker.
Using built-in algorithms in Amazon SageMaker such as K-means clustering and PCA
Using custom container to deploy models in Amazon SageMaker.

Best Practices: Tips and tricks

Tips and tricks for working with Amazon SageMaker.
Troubleshooting common issues that may arise while working with Amazon SageMaker
Optimizing for cost and performance while working with Amazon SageMaker.

Real-world use cases: Working with SageMaker in various industry verticals

Understand how Amazon SageMaker can be used in various industry verticals.
Learn how to use Amazon SageMaker to solve real-world problems in healthcare, re

Hands-on-lab: Students will work on real-world use cases and projects

How to apply the knowledge learned throughout the course to real-world problems

Extra Material

AWS SageMaker Action Hub Integrations
Hands-on MasterClass: SageMaker Import your Own ML Code