The Machine Learning Pipeline on AWS

The Machine Learning Pipeline on AWS

Taking ML models from conceptualization to production is typically complex and time-consuming. You have to manage large amounts of data to train the model, choose the best algorithm for training it, manage the compute capacity while training it, and then deploy the model into a production environment. SageMaker reduces this complexity by making it much easier to build and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You will learn about each phase of the pipeline from instructor presentations and demonstrations.  You will then apply that knowledge to fraud detection, recommendation engines, or flight delays. By the end of the course, you will have successfully built, trained, evaluated, tuned, and deployed an ML model that solves selected business problems.

Duration: 4 Days

Prerequisites

• Basic knowledge of Python
• Basic understanding of working in a Jupyter notebook environment
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
• AWS Technical Essentials (or equivalent experience with AWS)

Audience

• Developers
• Solutions Architects
• Data Engineers
• Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning

Learning Objectives

• Select and justify the appropriate ML approach for a given business problem
• Use the ML pipeline to solve a specific 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 machine learning to a real-life business problem after the course is complete

Topics

• Introduction to Machine Learning and the ML Pipeline
• Introduction to Amazon SageMaker
• Problem Formulation
• Preprocessing
• Model Training
• Model Evaluation
• Feature Engineering and Model Tuning
• Deployment