Amazon Web Services (AWS)
Machine Learning Pipeline on AWS
Solve a real business problem with machine learning over four days.
4 daysIntermediate9 modulesAWS Certified Machine Learning - Specialty
About this course
Solve a real business problem with machine learning over four days. Working on a project of your choice, you will frame the problem, prepare data in S3 and SageMaker, engineer features, train, evaluate, tune and deploy a model, following best practice throughout.
What you'll learn
- 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
Curriculum
9 modules
- Review AWS training format and course materials
- Explore your choice of projects: fraud detection, recommendation engines, or flight delays
- Explore the phases of the pipeline and ML workflow
- Learn how to use machine learning in real-world business contexts
- Tour of Amazon SageMaker features and environments
- Overview of SageMaker Studio, notebooks, and automation tools
- Align business goals with machine learning models
- Learn how to automate problem formulation and pipeline setup
- Clean and transform raw data
- Use SageMaker Processing and Amazon S3 storage efficiently
- Launch training jobs with built-in or custom algorithms
- Configure compute environments and resource settings
- Apply evaluation metrics like confusion matrices and AUC
- Tune an ML model using SageMaker automatic model tuning
- Apply feature engineering techniques to improve model performance
- Learn how to automate repeatable stages of the ML pipeline
- Deploy an ML model to a SageMaker endpoint
- Review secure deployment patterns and scalable infrastructure in AWS
Who it's for
Developers, analysts and data scientists who want a project-based path into ML on AWS.
Before you start
- Basic understanding of AWS services (recommended)
- Familiarity with Python programming (helpful but not required)
- No prior machine learning experience required
Prepares for
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