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GCP Engineering and Streaming Architecture

GCP Engineering and Streaming Architecture

  • Online, Self-Paced

Course Description

Feature engineering can be an essential tool in applied machine learning when enhancing a dataset. In this course, you will learn about concepts of feature engineering, including areas of streaming architecture and implementations.

Learning Objectives

GCP Feature Engineering

  • start the course
  • describe the concepts of feature engineering
  • recall the benefits of quality features with feature engineering and feature selection
  • describe the process of input selection in feature engineering
  • demonstrate feature engineering in use cases

Streaming Architecture

  • recall the concepts of streaming data and real-time stream processing
  • describe Dataflow triggers and late data
  • install Java JDK on Windows 10
  • demonstrate how to install Apache Maven on Windows 10
  • install Google Cloud SDK and initialize SDK Shell on Windows 10
  • demonstrate the process of streaming pipelines using Dataflow SDK 2.x and Java in Cloud SDK Shell
  • demonstrate the process of streaming pipelines using Dataflow SDK 2.x and Python in Google Cloud Shell

Practice: Feature Engineering and Streaming

  • describe feature engineering concepts and streaming data architecture

Framework Connections

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The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.