Practical Data Science with Amazon SageMaker

Last Updated: 08-03-2025

The Practical Data Science with Amazon SageMaker course is designed for professionals looking to harness the power of Amazon SageMaker for building, training, and deploying machine learning models. This hands-on course takes you through the entire data science workflow, from data preprocessing and model development to deployment and monitoring. Learn how to leverage SageMaker’s comprehensive set of tools and capabilities, including built-in algorithms, Jupyter notebooks, and automated model tuning, to deliver powerful ML solutions in production environments.

Schedule & Fee
Learning Objectives
Prerequisites
Target Audience
Course Modules
FAQs

Schedule	Learners	Course Fee (Incl. of all Taxes)	Register Your Interest
August 04^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 8 Hours)		10% Off $320 $288 Fast Filling! Hurry Up.
August 11^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 8 Hours)		10% Off $320 $288
August 18^th - 19^th 06:00 PM - 10:00 PM (CST) Live Virtual Classroom (Duration : 8 Hours)		10% Off $320 $288
August 25^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 8 Hours) Guaranteed-to-Run		10% Off $320 $288
September 01^st 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 8 Hours)		20% Off $320 $256
September 08^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 8 Hours)		20% Off $320 $256
September 15^th - 16^th 06:00 PM - 10:00 PM (CST) Live Virtual Classroom (Duration : 8 Hours)		20% Off $320 $256
September 22^nd 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 8 Hours) Guaranteed-to-Run		20% Off $320 $256

Course Prerequisites

Basic understanding of machine learning concepts and algorithms.
Familiarity with Python and data manipulation libraries (e.g., Pandas, NumPy).
Recommended: Experience with AWS services like S3, EC2, and IAM is helpful but not required.

Learning Objectives

By the end of this course, you will be able to:

Understand the complete data science workflow and apply it using Amazon SageMaker.
Preprocess, clean, and prepare data for machine learning using SageMaker Data Wrangler.
Build and train machine learning models using SageMaker’s built-in algorithms or custom models.
Optimize model performance with SageMaker Automatic Model Tuning (Hyperparameter optimization).
Deploy machine learning models into production with SageMaker Hosting and manage endpoints for real-time predictions.
Monitor and maintain models in production using SageMaker Model Monitor and SageMaker Pipelines.
Leverage SageMaker Studio for end-to-end development, collaboration, and management of machine learning projects.
Implement best practices for securing, scaling, and automating data science workflows on AWS.

Target Audience

This course is perfect for:

Data scientists and machine learning engineers looking to gain hands-on experience with Amazon SageMaker.
Professionals transitioning into the field of data science who want to work with real-world ML workflows on AWS.
Machine learning practitioners who want to streamline the process of training, deploying, and monitoring ML models.
Data analysts and business intelligence professionals interested in applying machine learning for actionable insights.

Course Modules

Introduction to Machine Learning:
- Benefits of machine learning (ML)
- Types of ML approaches
- Framing business problems for ML solutions
- Understanding prediction quality
- Processes, roles, and responsibilities in ML projects
Preparing a Dataset:
- Data analysis and visualization techniques
- Data preparation tools and methodologies
- Hands-on with Amazon SageMaker Studio and Notebooks
- Data preparation using SageMaker Data Wrangler
Training a Model:
- Steps involved in training ML models
- Selecting appropriate algorithms
- Training models using Amazon SageMaker
- Hands-on lab: Training a model with SageMaker
- Utilizing Amazon CodeWhisperer for code suggestions
- Demonstration of Amazon CodeWhisperer in SageMaker Studio Notebooks
Evaluating and Tuning a Model:
- Techniques for model evaluation
- Hyperparameter tuning and optimization
- Hands-on lab: Model tuning with SageMaker
Deploying a Model:
- Model deployment strategies
- Hands-on lab: Deploying a model to a real-time endpoint and generating predictions
Operational Challenges:
- Responsible ML practices
- Roles and responsibilities in ML teams and MLOps
- Automation in ML workflows
- Monitoring and updating deployed models

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What Our Learners Are Saying

The training, courseware, and lab experience were insightful and valuable. Keep up the great work and learning experience!

Nitish A. Anand – Accenture

Course: SC-200: Microsoft Security Operations Analyst
Date: 15th Jan 2025

The instructor was professional and very content.

Justine Daudi Mlimbilah – Bank of Africa, Tanzania

Course: MD-102: Microsoft 365 Endpoint Administrator
Date: 20th Dec 2024

The instructor was so knowledgeable & humble. Rare to find someone so confident but so down to earth these days. So appreciative to him.”

Mohd. Hassan – Ministry of Finance, UAE

Course: AZ-700: Designing and Implementing Microsoft Azure Networking Solutions
Date: 31st July 2024

Instructor is experienced and knowledgeable in guiding.

Dharshini Mahalaxmi – Dr. MGR Education and Research Institute, Chennai, India

Course: SC-300: Microsoft Identity and Access Administrator
Date: 4th May 2024