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Pluralsight

Data Engineering with AWS Machine Learning

via Pluralsight

Overview

The whole field of machine learning revolves around data. This course will teach you how to properly choose between the various AWS data repositories, ingestion services, and transformation services in a cost-effective, best-practice manner.

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.

Topics:
  • Course Overview
  • Important Data Characteristics to Consider in a Machine Learning Solution
  • Typical Data Flow for Machine Learning on AWS
  • Data Storage Options for Machine Learning on AWS
  • Database Options for Machine Learning on AWS
  • Using a Data Warehouse or a Data Lake as a Machine Learning Repository
  • Streaming Data Ingestion Solutions on AWS for Machine Learning
  • Batch Data Ingestion Solutions on AWS for Machine Learning
  • Data Transformation Overview on AWS for Machine Learning
  • Data-driven Workflows: The AWS Data Pipeline
  • Data Transformation Using Apache Spark on Amazon EMR
  • Data Transformation Using Serverless AWS Glue and Serverless Amazon Athena

Taught by

Kim Schmidt

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