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Google Cloud Fundamentals: Big Data and Machine Learning

(Formerly CPB100)

(1 day)

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, participants 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. A personal laptop is required for all workshops and will not be provided.

Learning Objectives

This course teaches participants the following skills:

• Identify the 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

Audience

This class is intended for the following participants:

• Data analysts, Data scientists, 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

Prerequisites

To get the most of out of this course, participants should have:

• 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 Outline

Module 1: Introducing Google Cloud Platform

• Google Platform Fundamentals Overview
• Google Cloud Platform Data Products and Technology
• Usage scenarios
• Lab: Sign up for Google Cloud Platform

Module 2: Compute and Storage Fundamentals

• CPUs on demand (Compute Engine)
• A global filesystem (Cloud Storage)
• CloudShell
• Lab: Set up a Ingest-Transform-Publish data processing pipeline

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
• Lab: Machine Learning Recommendations with SparkML

Module 4: Scaling Data Analysis

• Fast random access
• Datalab
• BigQuery
• Lab: Build machine learning dataset
• Machine Learning with TensorFlow
• Lab: Train and use neural network
• Fully built models for common needs
• Lab: Employ ML APIs

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