IT Skills     Cloud Computing and Virtualization     Amazon     AWS Certified Machine Learning

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Creating a data pipeline is essential to making any data-related product. AWS Data Pipeline, AWS Batch, and AWS Workflow frameworks allow you to manage data using ETL data management across various AWS tools and services, making AWS a perfect platform for combining data from multiple sources.
In this course, you'll learn how to automate data movement and transformation processes on AWS and define data-driven pipelines and workflows. Investigating how data pipelines enable seamless, scalable, and fault-tolerant data transfer between AWS storage and computational tools helps illuminate the full potential of AWS in machine learning.
By the end of this course, you'll have a working knowledge of the most common use cases of AWS Data Pipeline, AWS Batch, and AWS Workflow, bringing you closer to being fully prepared for the AWS Certified Machine Learning - Specialty certification exam.

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Objectives

AWS Certified Machine Learning: Data Pipelines & Workflows discover the key concepts covered in this course describe how AWS data pipelines work and name their main benefits configuring an AWS Data Pipeline application using the AWS console compare the functionalities of AWS Data Pipeline and AWS Glue describe the functionality of AWS Batch and its main benefits specify real-world use cases of AWS Batch describe how AWS Step Functions works and name the principles behind workflow design specify real-world use cases of AWS Step Functions work with AWS Step Functions to manage a batch job describe how real-time and video data engineering pipelines work describe how batch processing and analytics data engineering pipelines work summarize the key concepts covered in this course