Machine Learning Pipeline on AWS Training

Price
$2,700.00 USD

Duration
4 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

Add Exam Voucher
$300.00

Course Overview

Struggling to turn machine learning potential into business impact? This course helps you bridge the gap with real projects and proven AWS tools.

According to Gartner, 80% of enterprises will operationalize AI using APIs or low-code tools by 2026—up from just 25% in 2023. The Machine Learning Pipeline on AWS course equips you to join that transformation through a complete, hands-on experience building and deploying ML solutions using Amazon SageMaker.

You’ll learn about the ML pipeline and apply each phase—problem formulation, data preparation, model training, evaluation, tuning, and deployment—to solving one of three business problems: fraud detection, recommendation engines, or flight delays. With a strong focus on using Amazon SageMaker and AWS Cloud services like Amazon S3, you’ll build a fully functional and scalable pipeline to solve a specific business problem.

This AWS training provides the skills and tools to complete a project and deploy an ML model using Amazon SageMaker—no prior experience required.

Course Objectives

This course teaches you how to use the ML pipeline to solve a specific business problem using Amazon SageMaker. You’ll gain the skills to:

  • Justify the appropriate ML approach for a given business problem
  • Use the ML pipeline to solve a specific selected business problem
  • Train, evaluate, deploy, and tune an ML model in Amazon SageMaker
  • Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
  • Apply your knowledge to complete a project by solving one of three business problems using the AWS Cloud

Who Should Attend?

This course is ideal for developers, solutions architects, data engineers, and IT professionals who want to learn how to build and deploy machine learning pipelines on AWS. It’s appropriate for those with little to no experience who want to solve real-world problems using Amazon SageMaker.

  • Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
  • Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
  • Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
  • Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
  • Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
  • Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

What does this AWS training cover in the context of machine learning pipelines?

This course covers the full machine learning pipeline on AWS using Amazon SageMaker. Each phase of the pipeline—from data preparation to deployment—is taught through hands-on exercises designed to build real-world machine learning models in the AWS Cloud.

How will I learn to use Amazon SageMaker for ML workflows?

You’ll work directly with Amazon SageMaker features in guided labs and exercises. You’ll learn how to automate ML workflows, train and evaluate models, and manage endpoints for scalable deployment—all using SageMaker tools and environments.

Will I learn to deploy an ML model using SageMaker?

Yes. Model deployment is a major focus of the course. You’ll learn how to deploy an ML model using Amazon SageMaker endpoints, manage inference, and apply best practices for secure, cost-optimized deployment in the AWS Cloud.

Does this course include hands-on exercises for real ML projects like fraud detection?

Absolutely. You’ll select one of three business use cases—fraud detection, recommendation engines, or flight delays—and complete hands-on exercises that apply every phase of the ML pipeline using Amazon SageMaker.

What makes this AWS course valuable for professionals working with machine learning models?

This course offers a complete learning experience—from instructor presentations to hands-on labs—focused on solving real business problems using SageMaker. You’ll gain practical experience in model development, evaluation, and deployment in the AWS Cloud.

Course Prerequisites

  • Basic understanding of AWS services (recommended)
  • Familiarity with Python programming (helpful but not required)
  • No prior machine learning experience required

Agenda

Module 0: Course Kickoff and Project Selection

  • Review AWS training format and course materials
  • Explore your choice of projects: fraud detection, recommendation engines, or flight delays

Module 1: Understanding Machine Learning Pipelines

  • Explore the phases of the pipeline and ML workflow
  • Learn how to use machine learning in real-world business contexts

Module 2: Using Amazon SageMaker for ML Workflows

  • Tour of Amazon SageMaker features and environments
  • Overview of SageMaker Studio, notebooks, and automation tools

Module 3: Framing ML Problems in the AWS Cloud

  • Align business goals with machine learning models
  • Learn how to automate problem formulation and pipeline setup

Module 4: Data Preparation Using SageMaker and Amazon S3

  • Clean and transform raw data
  • Use SageMaker Processing and Amazon S3 storage efficiently

Module 5: Training and Inference in Amazon SageMaker

  • Launch training jobs with built-in or custom algorithms
  • Configure compute environments and resource settings

Module 6: Evaluate ML Models and Tune Parameters

  • Apply evaluation metrics like confusion matrices and AUC
  • Tune an ML model using SageMaker automatic model tuning

Module 7: Feature Engineering and Automation Best Practices

  • Apply feature engineering techniques to improve model performance
  • Learn how to automate repeatable stages of the ML pipeline

Module 8: ML Model Deployment in Amazon SageMaker

  • Deploy an ML model to a SageMaker endpoint
  • Review secure deployment patterns and scalable infrastructure in AWS
 

Get in touch to schedule training for your team
We can enroll multiple students in an upcoming class or schedule a dedicated private training event designed to meet your organization’s needs.

 



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