The Machine Learning Pipeline on AWS

The Machine Learning Pipeline on AWS is an intermediate-level, four-day course that explores the iterative machine learning pipeline through a project-based learning approach. Learners work through the major phases of an ML project, from problem formulation and data preprocessing through model training, evaluation, tuning, and deployment using Amazon SageMaker.

The course combines presentations, group exercises, demonstrations, and hands-on labs. Learners apply the ML pipeline to a selected business problem, such as fraud detection, recommendation engines, or flight delays, and develop an ML model using Amazon SageMaker. AWS identifies this as a four-day classroom course focused on solving real business problems using the ML pipeline.

• Select and justify an appropriate machine learning approach for a given business problem
• Apply the machine learning pipeline to solve a specific business problem
• Use Amazon SageMaker and Jupyter notebooks for machine learning workflows
• Formulate business problems as appropriate machine learning problems
• Preprocess, integrate, and visualize data for machine learning
• Train machine learning models using Amazon SageMaker
• Evaluate classification and regression models
• Apply feature engineering and hyperparameter tuning techniques
• Deploy and perform inference with machine learning models using Amazon SageMaker
• Describe best practices for scalable, cost-optimized, and secure ML pipelines in AWS

• Basic knowledge of Python programming
• Basic understanding of AWS Cloud infrastructure, including Amazon S3 and Amazon CloudWatch
• Basic experience working in a Jupyter notebook environment
• Basic knowledge of statistics is helpful