The Machine Learning Pipeline on AWS
E-Learning
Description
This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
In this course, you will learn to:
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 using 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
This course is intended for:
Developers
Solutions Architects
Data Engineers
Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon
SageMaker
| Lesson Id | Title | Description |
|---|---|---|
| 1 | Module 0: Introduction | Pre-assessment |
| 2 | Module 1: Introduction to Machine Learning and the ML Pipeline |
Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach |
| 3 | Module 2: Introduction to Amazon SageMaker |
Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks |
| 4 | Module 3: Problem Formulation |
Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects |
| 5 | Module 4: Preprocessing |
Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects |
| 6 | Module 5: Model Training |
Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker |
| 8 | Module 7: Feature Engineering and Model Tuning |
Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations |
| 9 | Module 8: Deployment |
How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up |
| 7 | Module 6: Model Evaluation |
How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations |
Self-Paced
Free
This course includes: :
Full lifetime access