Storing data for machine learning is challenging due to the varying formats and characteristics of data. Raw ingested data must first be transformed into the format necessary for downstream machine learning consumption, and once the data is ready to be used, it must be ingested from storage to the machine learning service. In this course, Data Engineering with AWS Machine Learning, you’ll learn to choose the right AWS service for each of these data-related machine learning ML tasks for any given scenario. First, you’ll explore the wide variety of data storage solutions available on AWS and what each type of storage is used for. Next, you’ll discover the differing AWS services used to ingest data into ML-specific services and when to use each one. Finally, you’ll learn how to transform your raw data into the proper formats used by the various AWS ML services. When you’re finished with this course, you’ll have the skills and knowledge of how to properly provide data solutions for storing, preparing, and ingesting data needed to architect data engineering solutions on AWS for Machine Learning, and be prepared to take the AWS Machine Learning Certification exam.
Author Name: Kim Schmidt
Author Description:
Kim Schmidt has been working in the technology industry for more than 12 years with an extremely broad array of titles and using very diverse technologies. She holds many industry certifications and awards. Companies Kim has worked for or with include Microsoft, Dun & Bradstreet, Google, Amazon Web Services, and a couple of Augmented Reality companies. Kim is the Founder and CEO of DataLeader, an AWS Partner and Vendor company. DataLeader focuses on AWS Big Data Architecture, Data Solutions,… more