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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.
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.
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
Course Outline
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