Course Code



21 Hours


  • Experience with application development
  • Familiarity with Amazon Web Services (AWS) Console


  • Data scientists
  • Developers


Amazon Web Services (AWS) SageMaker is a cloud machine learning service that lets developers build, train, and deploy machine learning models quickly at any scale.

This instructor-led, live training (online or onsite) is aimed at data scientists and developers who wish to create and train machine learning models for deployment into production-ready hosting environments.

By the end of this training, participants will be able to:

  • Use notebook instances to prepare and upload data for training.
  • Train machine learning models using training datasets.
  • Deploy trained models to an endpoint to create predictions.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

     Course Outline


  • Understanding machine learning with SageMaker
  • Machine learning algorithms

Overview of AWS SageMaker Features

  • AWS and cloud computing
  • Models development

Setting up AWS SageMaker

  • Creating an AWS account
  • IAM admin user and group

Familiarizing with SageMaker Studio

  • UI overview
  • Studio notebooks

Preparing Data Using Jupyter Notebooks

  • Notebooks and libraries
  • Creating a notebook instance

Training a Model with SageMaker

  • Training jobs and algorithms
  • Data and model parallel trainings
  • Post-training bias analysis

Deploying a Model in SageMaker

  • Model registry and model monitor
  • Compiling and deploying models with Neo
  • Evaluating model performance

Cleaning Up Resources

  • Deleting endpoints
  • Deleting notebook instances


Summary and Conclusion


     Feedback (13)

I genuinely liked the new technology.


Practice parts

- Instytut Lotnictwa

The training is practical and it is good for understanding how to use AWS step by step


That it was all new technology and offerings to myself. After being shown how quick and easy it is to set up certain services in AWS, I feel I could get a real benefit out of it for quick project and proposal prototyping.

MDA Systems Ltd.

Fernando knew the products and how to use them. His willingness and friendliness to assist in the hands-on lab was great.

MDA Systems Ltd.

There was a good general pass over what seemed like the most important parts of AWS. The instructor was open to questions and addressed areas of AWS that were not part of the outline based on our questions.

MDA Systems Ltd.

I liked getting to understand the breadth of the services offered by AWS and gaining a better understanding of their pricing model for each of those services.

William Crowdis - MDA Systems Ltd.

Thought it was a good overview of a lot of different services. Liked the detail on IAS.

MDA Systems Ltd.

Explaining why it's financially viable to do these things

MDA Systems Ltd.

It provided context for the things we do in AWS.

MDA Systems Ltd.

Everything. I had played around with AWS before but just on my own personal time. The training really brought everything together, with real world examples and how many individual pieces can be bolted together for a applicable solution.

Matt Sartain - MDA Systems Ltd.

Hands-on labs

MDA Systems Ltd.

examples, preparation of materials, level of knowledge of trainer, excellent communication

Michał Krasucki - Instytut Lotnictwa

The course could be tailored to suit your needs and objectives. It can also be delivered on your premises if preferred.



Online Price per participant 6000 AED


Classroom Price per participant 6000 AED





  Workday courses take place between 9:30 and 16:30


  Show venue details

Number of Participants

Related Courses

Total Courses 12

AdaBoost Python for Machine Learning

  14 hours

Predictive Analytics

Predictive Analytics

What is Predictive Analytics?

Artificial Intelligence (AI) with H2O

  14 hours


  7 hours

RapidMiner for Machine Learning and Predictive Analytics

  14 hours


AutoML with Auto-Keras

  14 hours


  14 hours

Google Cloud AutoML

  7 hours


AutoML with Auto-sklearn

  14 hours


Pattern Recognition

  21 hours


Data Mining with Weka

  14 hours


H2O AutoML

  14 hours

Machine Learning for Mobile Apps using Google’s ML Kit

  14 hours

Pattern Recognition

Pattern Recognition

What is Pattern Recognition?

Pattern Matching

  14 hours


Machine Learning with Random Forest

  14 hours


Apache SystemML for Machine Learning

  14 hours

Public Course Discounts

Total Courses 5