پلتفرم ابری گوگل برای علم داده: دوره فشرده‌ای در مورد کلان داده، یادگیری ماشین و خدمات تحلیل داده

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 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 df-Capture.PNG