SkillSoft Explore Course – Lambers, Inc.

Aspire AI Apprentice to AI Architect AI Architect Track 3: AI Practitioner

The Amazon Machine Learning framework allows you to quickly deploy machine learning models using Amazon Web Services, automate model deployment and maintenance, and configure other Amazon tools to work in synchronicity. AI practitioners should consider the benefits and best practices of working with Amazon ML and other Amazon services in their AI development projects.

In this course, you'll explore advanced techniques for working with the Amazon ML framework. You'll examine the significant differences between Amazon ML and other frameworks. You'll recognize the advantages of using the Amazon ML platform for certain projects and identify the Amazon ML workflow. Finally, you'll complete a project developing and training an AI model using the Amazon ML framework, and troubleshoot typical problems that come up during model training and evaluation.

Asset ID	it_aiexamdj_01_enus
Course Type	Video Course
Course Category	AI Apprentice to AI Architect

Objectives
Extending Amazon Machine Learning discover the key concepts covered in this course specify cases in which it is advantageous to use Amazon ML over other platforms compare the use of Amazon ML and Google Cloud Platform compare the use of Amazon ML and Azure ML identify possible data sources for working with Amazon ML describe the capabilities of Amazon ML in relation to feature processing specify multiple approaches to how data can be split using Amazon ML list model types present in Amazon ML and specify their purposes describe the process of batch prediction in Amazon ML and identify cases in which batch prediction is more desirable than online prediction describe how real-time prediction is made in Amazon ML troubleshoot common problems and identify approaches to improve model accuracy in Amazon ML describe Sagemaker requirements and problem to be solved demonstrate Sagemaker model training and validation validate the results of the Sagemaker model demo summarize the key concepts covered in this course

Objectives

Extending Amazon Machine Learning

discover the key concepts covered in this course
specify cases in which it is advantageous to use Amazon ML over other platforms
compare the use of Amazon ML and Google Cloud Platform
compare the use of Amazon ML and Azure ML
identify possible data sources for working with Amazon ML
describe the capabilities of Amazon ML in relation to feature processing
specify multiple approaches to how data can be split using Amazon ML
list model types present in Amazon ML and specify their purposes
describe the process of batch prediction in Amazon ML and identify cases in which batch prediction is more desirable than online prediction
describe how real-time prediction is made in Amazon ML
troubleshoot common problems and identify approaches to improve model accuracy in Amazon ML
describe Sagemaker requirements and problem to be solved
demonstrate Sagemaker model training and validation
validate the results of the Sagemaker model demo
summarize the key concepts covered in this course