Google Cloud Fundamentals:ï¿½ Big Data and Machine Learning

Google Cloud Fundamentals: Big Data and Machine Learning

COURSE OUTLINE:

Description

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.

Audience

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

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

Learning Objectives

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

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

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