In order to build machine learning (ML) applications, it is important to formulate problems and collect data. Examine the choice between the online and on-premise implementation of the problem formulation and data collection phases through this course.
Explore how SageMaker algorithms help complete ML projects efficiently and work with various approaches that implement recommender systems. You'll also investigate how and when to use AWS data storage services and learn more about analyzing dataset readiness.
After taking this course, you'll be able to describe the advantages and disadvantages of using the cloud over an on-premise solution and define the problem formulation and success evaluation processes. You'll also be a step closer to preparing for the AWS Certified Machine Learning - Specialty certification exam.
Learning Objectives
{"discover the key concepts covered in this course"}