Practical Data Science with Amazon SageMaker: Build, Train and Deploy Machine Learning Models on AWS.

  • Why get trained: Learn the full data science workflow using Amazon SageMaker, from data preparation and feature engineering to model training, tuning and deployment.
  • Why it matters: Hands-on machine learning skills help teams turn data into actionable insights and deploy models that support real business use cases.
  • Who should attend: Data scientists, machine learning practitioners, developers, engineers and architects working with data-driven applications on AWS.

Build practical machine learning capability develop skills to deliver real-world ML solutions on AWS. HRD Corp Claimable.

Overview

Learn how to solve a real-world use case with machine learning (ML) and produce actionable results using Amazon SageMaker.

Learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.

Trainocate is an AWS Authorized Training Partner as well as the AWS Global Training Partner of the Year 2022-2025 is trusted by AWS to offer, deliver, and/or incorporate official AWS Training, including classroom and digital offerings. Whether your team prefers to learn from live instructors, on-demand courses, or both, ATPs offer a breadth of AWS Training options for learners of all levels.

 

Skills Covered

In this course, you will learn how to:

  • Apply Amazon SageMaker to a specific use case and dataset
  • Practice all the steps of the typical data science process
  • Visualize and understand the dataset
  • Explore how the attributes of the dataset relate to each other
  • Prepare the dataset for training
  • Use built-in algorithms
  • Train models with Amazon SageMaker using built-in algorithms
  • Explore results and performance of the model, and demonstrate how it can be tuned and executed outside of SageMaker
  • Run predictions on a batch of data with Amazon SageMaker
  • Deploy a model to an endpoint in Amazon SageMaker for real-time predictions
  • Learn how to configure an endpoint for serving predictions at scale
  • Understand Hyperparameter Optimization (HPO) with Amazon SageMaker to find optimal model parameters
  • Understand how to perform A/B model testing using Amazon SageMaker
  • Perform the domain-specific cost of errors analysis to further tune the model threshold in order to maximize model utility expressed in financial terms

Prerequisites

We recommend that attendees of this course have the following prerequisites:

  • Experience with Python programming language
  • Familiarity with NumPy and Pandas Python libraries is a plus
  • Familiarity with fundamental machine learning algorithms
  • Familiarity with productionizing machine learning models

Target Audience

This course is intended for:

  • Data science practitioners
  • Machine learning practitioners
  • Developers and engineers
  • Systems architects

Course Curriculum

Module 1: Introduction to Machine Learning

  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline

Module 2: Introduction to Data Prep and SageMaker

  • Training and test dataset defined
  • Introduction to SageMaker
  • Demonstration: SageMaker console
  • Demonstration: Launching a Jupyter notebook

Module 3: Problem Formulation and Dataset Preparation

  • Business challenge: Customer churn
  • Review customer churn dataset

Module 4: Data Analysis and Visualization

  • Demonstration: Loading and visualizing your dataset
  •  Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demonstration: Cleaning the data

Module 5: Training and Evaluating a Model

  • Types of algorithms
  • XGBoost and SageMaker
  • Demonstration: Training the data
  • Exercise 3: Finishing the estimator definition
  • Exercise 4: Setting hyper parameters
  • Exercise 5: Deploying the model
  • Demonstration: hyper parameter tuning with SageMaker
  • Demonstration: Evaluating model performance

Module 6: Automatically Tune a Model

  • Automatic hyper parameter tuning with SageMaker
  • Exercises 6-9: Tuning jobs

Module 7: Deployment / Production Readiness

  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling
  • Demonstration: Configure and test auto scaling
  • Demonstration: Check hyper parameter tuning job
  • Demonstration: AWS Auto Scaling
  • Exercise 10-11: Set up AWS Auto Scaling

Module 8: Relative Cost of Errors

  • Cost of various error types
  • Demo: Binary classification cutoff

Module 9: Amazon SageMaker Architecture and Features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

Dates & Locations

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August 20, 2026 - August 20, 2026

Location: Kuala Lumpur
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August 20, 2026 - August 20, 2026

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November 13, 2026 - November 13, 2026

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November 13, 2026 - November 13, 2026

Location: Online
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RM 1800
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  • Prove your expertise with a globally respected credential recognized by employers worldwide.
  • Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.

Explore our top AWS Certifications for 2026 and start building the skills that matter today.

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AWS also helps provide the answer to another critical questions in your cloud journey: “How to get to the cloud in the fastest, most efficient way possible?” A transformative migration is now within your reach. Now you can learn how you can migrate with your confidence by building your team’s cloud skills with AWS Training and certification by downloading this e-book.

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Frequently Asked Questions

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Having the skills and insight to act upon your creativity and inspiration is essential to discovering new business opportunities in the cloud and helping your organization innovate. You’ll be able to experiment with AWS Cloud services, quickly testing products and implementing strategies to determine what best delivers your organizational goals.

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Learn about each AWS Certification in our AWS Training and Certification blog post. To answer that question, you’ll need to consider your role and honestly evaluate your existing level of experience and knowledge across various areas of technical and cloud expertise. AWS offers four categories of certifications, three of which correspond to the amount of experience you have with the AWS Cloud. For Specialty certifications, the amount of recommended experience varies by certification.

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