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