• Online, Self-Paced
Course Description

You can create and manage an assortment of data processes and network models using GCP. This course will go through the various types, including using a GPU and TensorFlow to create and manage GPU and instances.

Learning Objectives

GCP Network Modeling

  • start the course
  • define the types of virtual networks and the benefits of each
  • specify the process for creating a network

Machine Learning with TensorFlow GPU

  • recall the process for using TensorFlow with GPU
  • describe the various machine learning APIs and their uses

Data Processing Architecture

  • describe Dataflow and how it can be used to create data processing streams
  • recognize differences between Pub and Sub message middleware and when to use them
  • define the various pipelines for Dataflow processing
  • demonstrate the process of creating Dataflow pipelines in GCP
  • specify the differences between real-time and batch data processing

Practice: Data Analysis and Processing

  • recognize more concepts in analysis of data processing in GCP

Framework Connections

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.