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.
Course Overview [Autogenerated] Hi, everyone. My name is Kim Schmidt and welcome to my course data engineering for AWS Machine Learning. I am an AWS Data and AI expert at data Leader. Making quality data available in a reliable manner is a major determinant of success for machine learning initiatives. Data engineers are test with this huge responsibility and they need to know the data and application characteristics to look for when making choices of what AWS service to use. In a specific scenario, this depends on many other factors that need to be thoroughly understood. This course will teach you everything you need to know to do data engineering on AWS for machine learning successfully, Some of the major topics that we will cover include how to choose the best data repository, ingestion service and transformation service for every use case possible. What machine learning use cases can be used with different AWS services used for data engineering, different ways to perform analytics and e. T. L on batch and streaming data. How to automate many of these tasks and how all the US services used in machine learning scenarios work together. By the end of this course, you'll understand completely how to be a successful data engineer for AWS Machine Learning and pass that ML exam with flying colors Before beginning this course, you should be familiar with AWS security networking databases and analytics. From here, you should feel comfortable diving in to other courses on feature engineering, exploratory data analysis and data modeling and machine learning implementation for all AWS machine learning. I hope you'll join me on this journey to learn these important and fascinating topics with the data engineering for A W S Machine Learning Course at Pluralsight.