Google Cloud Platform for Data Science: A Crash Course on Big Data, Machine Learning, and Data Analytics Services

✍ Scribed by Shitalkumar R. Sukhdeve, Sandika S. Sukhdeve

• Publisher: Apress
• Year: 2023
• Tongue: English
• Leaves: 231
• Category: Library

✦ Synopsis

This book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for Data Science, using only the free tier services offered by the platform.

Data Science and Machine Learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of Data Science services that can be used to store, process, and analyze large datasets, and train and deploy Machine Learning models.

The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for Data Science and Big Data projects.

Readers will learn how to set up a Google Colaboratory account and run Jupyter notebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy Machine Learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL.

Google Colaboratory, or Colab, is a robust cloud-based platform for Data Science. In the Chapter 2, we delve into the features and capabilities of Colab. You will learn how to create and run Jupyter notebooks, including Machine Learning models, leveraging Colab's seamless integration with GCP services. We also discuss the benefits of using Colab for collaborative data analysis and experimentation.

The Chapter 3 explores the world of big data and Machine Learning on GCP. We delve into BigQuery, a scalable data warehouse, and its practical use cases. Next, we focus on BigQuery ML, which enables you to build Machine Learning models directly within BigQuery. We then focus on Google Cloud AI Platform, where you will learn to train and deploy machine learning models. Additionally, we introduce TensorFlow, a popular framework for deep learning on GCP. Lastly, we explore Google Cloud Dataproc, which facilitates the efficient processing of large-scale datasets.

What You Will Learn:
Set up a GCP account and project
Explore BigQuery and its use cases, including machine learning
Understand Google Cloud AI Platform and its capabilities
Use Vertex AI for training and deploying machine learning models
Explore Google Cloud Dataproc and its use cases for big data processing
Create and share data visualizations and reports with Looker Data Studio
Explore Google Cloud Dataflow and its use cases for batch and stream data processing
Run data processing pipelines on Cloud Dataflow
Explore Google Cloud Storage and its use cases for data storage
Get an introduction to Google Cloud SQL and its use cases for relational databases
Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streaming

Who This Book Is For:
Data scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their Data Science and Big Data projects.

✦ Table of Contents

Table of Contents
About the Authors
About the Technical Reviewer
Acknowledgments
Preface
Introduction
Chapter 1: Introduction to GCP
Overview of GCP and Its Data Science Services
Setting Up a GCP Account and Project
Summary
Chapter 2: Google Colaboratory
Features of Colab
Creating and Running Jupyter Notebooks on Colaboratory
Hands-On Example
Importing Libraries
Working with Data
Visualize Data
Running Machine Learning Models on Colaboratory
Deploying the Model on Production
Accessing GCP Services and Data from Colaboratory
Summary
Chapter 3: Big Data and Machine Learning
BigQuery
Running SQL Queries on BigQuery Data
BigQuery ML
Google Cloud AI Platform and Its Capabilities
Using Vertex AI for Training and Deploying Machine Learning Models
Train a Model Using Vertex AI and the Python SDK
Introduction to Google Cloud Dataproc and Its Use Cases for Big Data Processing
How to Create and Update a Dataproc Cluster by Using the Google Cloud Console
TensorFlow
Summary
Chapter 4: Data Visualization and  Business Intelligence
Looker Studio and Its Features
Creating and Sharing Data Visualizations and Reports with Looker Studio
BigQuery and Looker
Building a Dashboard
Data Visualization on Colab
Summary
Chapter 5: Data Processing and Transformation
Introduction to Google Cloud Dataflow and Its Use Cases for Batch and Stream Data Processing
Running Data Processing Pipelines on Cloud Dataflow
Introduction to Google Cloud Dataprep and Its Use Cases for Data Preparation
Summary
Chapter 6: Data Analytics and  Storage
Introduction to Google Cloud Storage and Its Use Cases for Data Storage
Key Features
Storage Options
Storage Locations
Creating a Data Lake for Analytics with Google Cloud Storage
Introduction to Google Cloud SQL and Its Use Cases for Relational Databases
Create a MySQL Instance by Using Cloud SQL
Connect to Your MySQL Instance
Create a Database and Upload Data in SQL
Introduction to Google Cloud Pub/Sub and Its Use Cases for Real-Time Data Streaming
Setting Up and Consuming Data Streams with Cloud Pub/Sub
Summary
Chapter 7: Advanced Topics
Securing and Managing GCP Resources with IAM
Using the Resource Manager API, Grant and Remove IAM Roles
Using Google Cloud Source Repositories for Version Control
Dataplex
Cloud Data Fusion
Enable or Disable Cloud Data Fusion
Create a Data Pipeline
Summary
Bibliography
Index
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