Course details

GCP Network Data Processing Models

GCP Network Data Processing Models


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/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.

Target Audience
Data professionals who are responsible for provisioning and optimizing big data solutions, and data enthusiasts getting started with Google Cloud Platform

Prerequisites
None

Expected Duration (hours)
0.7

Lesson Objectives

GCP Network Data Processing Models

  • start the course
  • define the types of virtual networks and the benefits of each
  • specify the process for creating a network
  • recall the process for using TensorFlow with GPU
  • describe the various machine learning APIs and their uses
  • 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
  • recognize more concepts in analysis of data processing in GCP
  • Course Number:
    cl_gcde_a04_it_enus

    Expertise Level
    Intermediate