Live Chat
Monday - Friday 8am - 6pm EST Chat Now
Contact Us
Monday - Friday 8am - 8pm EST 1-866-716-6688 Other Contact Options
Checkout

Cart () Loading...

    • Quantity:
    • Delivery:
    • Dates:
    • Location:

    $

Google Cloud Fundamentals: Big Data and Machine Learning

Learn about the Google Cloud big data capabilities.

GK# 8325

Course Overview

TOP

This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, you will get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.

What You'll Learn

TOP
  • Purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform
  • Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform
  • Employ BigQuery and Cloud Datalab to carry out interactive data analysis
  • Train and use a neural network using TensorFlow
  • Employ ML APIs
  • Choose between different data processing products on the Google Cloud Platform

Outline

TOP
Viewing outline for:

Virtual Classroom Live Outline

1. Introducing Google Cloud Platform

  • Google Platform Fundamentals Overview
  • Google Cloud Platform Data Products and Technology
  • Usage scenarios

2. Compute and Storage Fundamentals

  • CPUs on demand (Compute Engine)
  • A global filesystem (Cloud Storage)
  • CloudShell

3. Data Analytics on the Cloud

  • Stepping-stones to the cloud
  • CloudSQL: your SQL database on the cloud
  • Lab: Importing data into CloudSQL and running queries
  • Spark on Dataproc

4. Scaling Data Analysis

  • Fast random access
  • Datalab
  • BigQuery
  • Machine Learning with TensorFlow
  • Fully built models for common needs

5. Data Processing Architectures

  • Message-oriented architectures with Pub/Sub
  • Creating pipelines with Dataflow
  • Reference architecture for real-time and batch data processing

6. Summary

  • Why GCP?
  • Where to go from here
  • Additional Resources

Labs

TOP
Viewing labs for:

Virtual Classroom Live Labs

Lab 1: Sign up for Google Cloud Platform

Lab 2: Set up a Ingest-Transform-Publish data processing pipeline

Lab 3: Machine Learning Recommendations with SparkML

Lab 4: Build machine learning dataset

Lab 5: Train and use neural network

Lab 6: Employ ML APIs

Prerequisites

TOP
  • Basic proficiency with common query language such as SQL
  • Experience with data modeling, extract, transform, load activities
  • Developing applications using a common programming language such Python
  • Familiarity with Machine Learning and/or statistics

Who Should Attend

TOP
  • Data analysts getting started with Google Cloud Platform
  • Data scientists getting started with Google Cloud Platform
  • Business analysts getting started with Google Cloud Platform
  • Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports
  • Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists
Course Delivery

This course is available in the following formats:

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 1 day

Request this course in a different delivery format.
Enroll