Automating Machine Learning on AWS
The course provides a comprehensive guide on how to build and manage a Machine Learning (ML) pipeline on Amazon Web Services (AWS). Students will gain practical skills through a series of modules covering introduction to ML and ML Pipeline, using Amazon SageMaker, problem formulation, data pre-processing, model training, model evaluation , feature engineering and model tuning, as well as model deployment. The course will conclude with a wrap-up session to review the key concepts covered during the course.
CODE: DSAI208
Category: Artificial Intelligence Course
DESCRIPTION
COURSE CONTENT
COURSE OBJECTIVES
ADDITIONAL INFORMATION
DESCRIPTION
Teaching methodology
The course includes educational laboratories in which each student will be able to carry out training exercises that will provide practical experience in the use of the tool, for each of the topics covered during the course.Prerequisites
- Basic knowledge of AWS
- Basic knowledge of Python
COURSE CONTENT
Below is an overview of the course contents:
- Introduction to ML and ML Pipeline.
- Introduction to Amazon SageMaker.
- Problem Formulation.
- Data pre-processing.
- Model training.
- Model Evaluation.
- Feature Engineering and Model Tuning.
- Model Deployment.
COURSE OBJECTIVES
At the end of the course, participants will be able to:
- Select and justify the most correct ML approach to a problem
- Build, train, evaluate, deploy, and fine-tune an ML model on AWS
- Describe best practices for building and managing a Machine Learning pipeline on AWS
- Identify steps to apply Machine Learning to real-world problems using services and tools on AWS
ADDITIONAL INFORMATION
Duration – 4 days
Delivery – in the classroom, on site, remotely
PC and SW requirements:
- Internet connection
- Web browser, Google Chrome
- Zoom
Language
- Instructor: English
- Laboratories: English
- Slides: English
ARTIFICIAL INTELLIGENCE COURSE CATALOG
ARTIFICIAL INTELLIGENCE COURSE CATALOG
