IL - Google Cloud Fundamentals: Big Data and Machine Learning

Course Overview
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.
Course Details
  • Duration:
  • Level: 200
Who this course is designed for
  • 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 Objectives

What You Will Learn
  • 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

Course Pre-Requisites

Prerequisites:
  • 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

Course Modules

Course Outline

Module 1: Introducing Google Cloud Platform

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

Module 2: Compute and Storage Fundamentals

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

Module 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

Module 4: Scaling Data Analysis

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

Module 5: Data Processing Architectures

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

Module 6: Summary

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

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