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Learning Google BigQuery

Eric Brown,Thirukkumaran Haridass

Author : Eric Brown,Thirukkumaran Haridass
Publisher : Packt Publishing Ltd
Page : 255 pages
File Size : 55,9 Mb
Release : 2017-12-22
Category : Computers
ISBN : 9781787286290

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Learning Google BigQuery by Eric Brown,Thirukkumaran Haridass Pdf

Get a fundamental understanding of how Google BigQuery works by analyzing and querying large datasets About This Book Get started with BigQuery API and write custom applications using it Learn how BigQuery API can be used for storing, managing, and query massive datasets with ease A practical guide with examples and use-cases to teach you everything you need to know about Google BigQuery Who This Book Is For If you are a developer, data analyst, or a data scientist looking to run complex queries over thousands of records in seconds, this book will help you. No prior experience of working with BigQuery is assumed. What You Will Learn Get a hands-on introduction to Google Cloud Platform and its services Understand the different data types supported by Google BigQuery Migrate your enterprise data to BigQuery and query it using the legacy and standard SQL techniques Use partition tables in your project and query external data sources and wild card tables Create tables and data sets dynamically using the BigQuery API Perform real-time inserting of records for analytics using Python and C# Visualize your BigQuery data by connecting it to third party tools such as Tableau and R Master the Google Cloud Pub/Sub for implementing real-time reporting and analytics of your Big Data In Detail Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data. You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you. This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems. Style and Approach This book follows a step-by-step approach to teach readers the concepts of Google BigQuery using SQL. To explain various data querying processes, large-scale datasets are used wherever required.

Google BigQuery: The Definitive Guide

Valliappa Lakshmanan,Jordan Tigani

Author : Valliappa Lakshmanan,Jordan Tigani
Publisher : O'Reilly Media
Page : 522 pages
File Size : 42,5 Mb
Release : 2019-10-23
Category : Computers
ISBN : 9781492044437

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Google BigQuery: The Definitive Guide by Valliappa Lakshmanan,Jordan Tigani Pdf

Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently. Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you’re not familiar with or prefer to focus on specific tasks, this reference is indispensable.

Learning Google BigQuery

Eric Brown,Thirukkumaran Haridass

Author : Eric Brown,Thirukkumaran Haridass
Publisher : Packt Publishing Ltd
Page : 255 pages
File Size : 54,5 Mb
Release : 2017-12-22
Category : Computers
ISBN : 9781787286290

Get Book

Learning Google BigQuery by Eric Brown,Thirukkumaran Haridass Pdf

Get a fundamental understanding of how Google BigQuery works by analyzing and querying large datasets About This Book Get started with BigQuery API and write custom applications using it Learn how BigQuery API can be used for storing, managing, and query massive datasets with ease A practical guide with examples and use-cases to teach you everything you need to know about Google BigQuery Who This Book Is For If you are a developer, data analyst, or a data scientist looking to run complex queries over thousands of records in seconds, this book will help you. No prior experience of working with BigQuery is assumed. What You Will Learn Get a hands-on introduction to Google Cloud Platform and its services Understand the different data types supported by Google BigQuery Migrate your enterprise data to BigQuery and query it using the legacy and standard SQL techniques Use partition tables in your project and query external data sources and wild card tables Create tables and data sets dynamically using the BigQuery API Perform real-time inserting of records for analytics using Python and C# Visualize your BigQuery data by connecting it to third party tools such as Tableau and R Master the Google Cloud Pub/Sub for implementing real-time reporting and analytics of your Big Data In Detail Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data. You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you. This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems. Style and Approach This book follows a step-by-step approach to teach readers the concepts of Google BigQuery using SQL. To explain various data querying processes, large-scale datasets are used wherever required.

Machine Learning with BigQuery ML

Alessandro Marrandino

Author : Alessandro Marrandino
Publisher : Packt Publishing Ltd
Page : 344 pages
File Size : 50,9 Mb
Release : 2021-06-11
Category : Computers
ISBN : 9781800562189

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Machine Learning with BigQuery ML by Alessandro Marrandino Pdf

Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML Key FeaturesGain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery MLLeverage SQL syntax to train, evaluate, test, and use ML modelsDiscover how BigQuery works and understand the capabilities of BigQuery ML using examplesBook Description BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML. What you will learnDiscover how to prepare datasets to build an effective ML modelForecast business KPIs by leveraging various ML models and BigQuery MLBuild and train a recommendation engine to suggest the best products for your customers using BigQuery MLDevelop, train, and share a BigQuery ML model from previous parts with AI Platform NotebooksFind out how to invoke a trained TensorFlow model directly from BigQueryGet to grips with BigQuery ML best practices to maximize your ML performanceWho this book is for This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.

Google Bigquery

Valliappa Lakshmanan,Jordan Tigani

Author : Valliappa Lakshmanan,Jordan Tigani
Publisher : Unknown
Page : 0 pages
File Size : 51,9 Mb
Release : 2019
Category : Big data
ISBN : 1492044458

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Google Bigquery by Valliappa Lakshmanan,Jordan Tigani Pdf

Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you'll examine how to analyze data at scale to derive insights from large datasets efficiently. Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you're not familiar with or prefer to focus on specific tasks, this reference is indispensable.

Google BigQuery: The Definitive Guide

Valliappa Lakshmanan,Jordan Tigani

Author : Valliappa Lakshmanan,Jordan Tigani
Publisher : "O'Reilly Media, Inc."
Page : 522 pages
File Size : 46,8 Mb
Release : 2019-10-23
Category : Computers
ISBN : 9781492044413

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Google BigQuery: The Definitive Guide by Valliappa Lakshmanan,Jordan Tigani Pdf

Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently. Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you’re not familiar with or prefer to focus on specific tasks, this reference is indispensable.

Learning Google Analytics

Mark Edmondson

Author : Mark Edmondson
Publisher : "O'Reilly Media, Inc."
Page : 342 pages
File Size : 43,5 Mb
Release : 2022-11-10
Category : Computers
ISBN : 9781098113056

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Learning Google Analytics by Mark Edmondson Pdf

Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Because rather than simply report what has happened, GA4's new cloud integrations enable more data activation—linking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations. Author Mark Edmondson, Google Developer Expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get guidance on how to implement them. You'll learn: How Google Cloud integrates with GA4 The potential use cases that GA4 integrations can enable Skills and resources needed to create GA4 integrations How much GA4 data capture is necessary to enable use cases The process of designing dataflows from strategy though data storage, modeling, and activation

Machine Learning with BigQuery ML

Alessandro Marrandino

Author : Alessandro Marrandino
Publisher : Packt Publishing Ltd
Page : 344 pages
File Size : 44,5 Mb
Release : 2021-06-11
Category : Computers
ISBN : 9781800562189

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Machine Learning with BigQuery ML by Alessandro Marrandino Pdf

Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML Key FeaturesGain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery MLLeverage SQL syntax to train, evaluate, test, and use ML modelsDiscover how BigQuery works and understand the capabilities of BigQuery ML using examplesBook Description BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML. What you will learnDiscover how to prepare datasets to build an effective ML modelForecast business KPIs by leveraging various ML models and BigQuery MLBuild and train a recommendation engine to suggest the best products for your customers using BigQuery MLDevelop, train, and share a BigQuery ML model from previous parts with AI Platform NotebooksFind out how to invoke a trained TensorFlow model directly from BigQueryGet to grips with BigQuery ML best practices to maximize your ML performanceWho this book is for This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.

Hands-On Machine Learning on Google Cloud Platform

Giuseppe Ciaburro,V Kishore Ayyadevara,Alexis Perrier

Author : Giuseppe Ciaburro,V Kishore Ayyadevara,Alexis Perrier
Publisher : Packt Publishing Ltd
Page : 489 pages
File Size : 40,6 Mb
Release : 2018-04-30
Category : Computers
ISBN : 9781788398879

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Hands-On Machine Learning on Google Cloud Platform by Giuseppe Ciaburro,V Kishore Ayyadevara,Alexis Perrier Pdf

Unleash Google's Cloud Platform to build, train and optimize machine learning models Key Features Get well versed in GCP pre-existing services to build your own smart models A comprehensive guide covering aspects from data processing, analyzing to building and training ML models A practical approach to produce your trained ML models and port them to your mobile for easy access Book Description Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems. What you will learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google’s pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications Who this book is for This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy

Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment (English Edition)

Navin Sabharwal,Amit Agrawal

Author : Navin Sabharwal,Amit Agrawal
Publisher : BPB Publications
Page : 159 pages
File Size : 41,6 Mb
Release : 2021-01-05
Category : Computers
ISBN : 9789388511926

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Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment (English Edition) by Navin Sabharwal,Amit Agrawal Pdf

A step-by-step guide to build machine learning and NLP models using Google AutoML KEY FEATURESÊ ¥Understand the basic concepts of Machine Learning and Natural Language Processing ¥Understand the basic concepts of Google AutoML, AI Platform, and Tensorflow ¥Explore the Google AutoML Natural Language service ¥Understand how to implement NLP models like Issue Categorization Systems using AutoML ¥Understand how to release the features of AutoML models as REST APIs for other applications ¥Understand how to implement the NLP models using the Google AI Platform DESCRIPTIONÊÊ Google AutoML and AI Platform provide an innovative way to build an AI-based system with less effort. In this book, you will learn about the basic concepts of Machine Learning and Natural Language Processing. You will also learn about the Google AI services such as AutoML, AI Platform, and Tensorflow, GoogleÕs deep learning library, along with some practical examples using these services in real-life scenarios. You will also learn how the AutoML Natural Language service and AI Platform can be used to build NLP and Machine Learning models and how their features can be released as REST APIs for other applications. In this book, you will also learn the usage of GoogleÕs BigQuery, DataPrep, and DataProc for building an end-to-end machine learning pipeline. Ê This book will give you an in-depth knowledge of Google AutoML and AI Platform by implementing real-life examples such as the Issue Categorization System, Sentiment Analysis, and Loan Default Prediction System. This book is relevant to the developers, cloud enthusiasts, and cloud architects at the beginner and intermediate levels. WHAT YOU WILL LEARNÊ By the end of this book, you will learn how Google AutoML, AI Platform, BigQuery, DataPrep, and Dapaproc can be used to build an end-to-end machine learning pipeline. You will also learn how different types of AI problems can be solved using these Google AI services. A step-by-step implementation of some common NLP problems such as the Issue Categorization System and Sentiment Analysis System that provide you with hands-on experience in building complex AI-based systems by easily leveraging the GCP AI services. Ê WHO IS THIS BOOK FORÊ This book is for machine learning engineers, NLP users, and data professionals who want to develop and streamline their ML models and put them into production using Google AI services. Prior knowledge of python programming and the basics of machine learning would be preferred. TABLE OF CONTENTS 1. Introduction to Artificial Intelligence 2. Introducing the Google Cloud Platform 3. AutoML Natural Language 4. Google AI Platform 5. Google Data Analysis, Preparation, and Processing Services AUTHOR BIOÊ Navin Sabharwal: Navin is an innovator, leader, author, and consultant in AI and Machine Learning, Cloud Computing, Big Data Analytics, Software Product Development, Engineering, and R&D. He has authored books on technologies such as GCP, AWS, Azure, AI and Machine Learning systems, IBM Watson, chef, GKE, Containers, and Microservices. He is reachable at [email protected]. Amit Agrawal: Amit holds a masterÕs degree in Computer Science and Engineering from MNNIT (Motilal Nehru National Institute of Technology, Allahabad), one of the premier institutes of Engineering in India. He is working as a principal Data Scientist and researcher, delivering solutions in the fields of AI and Machine Learning. He is responsible for designing end-to-end solutions and architecture for enterprise products. He is reachable at [email protected].

Google Certification Guide - Google Professional Machine Learning Engineer

Cybellium Ltd

Author : Cybellium Ltd
Publisher : Cybellium Ltd
Page : 171 pages
File Size : 48,6 Mb
Release : 2024-06-01
Category : Computers
ISBN : 9798871400807

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Google Certification Guide - Google Professional Machine Learning Engineer by Cybellium Ltd Pdf

Google Certification Guide - Google Professional Machine Learning Engineer Unlock the World of Machine Learning on Google Cloud Embark on a transformative journey to become a Google Professional Machine Learning Engineer with this comprehensive guide. Designed for those who aspire to master the application of machine learning techniques and tools in the Google Cloud environment, this book is an essential resource for professionals seeking to harness the power of ML in their projects and workflows. What Awaits Inside: Advanced ML Concepts and Practices: Dive deep into the world of machine learning on Google Cloud, covering services like AI Platform, TensorFlow, and BigQuery ML. Real-World Applications: Learn through practical scenarios and hands-on examples, illustrating the effective implementation of machine learning models and solutions on Google Cloud. Strategic Exam Preparation: Gain crucial insights into the certification exam's structure and content, complemented by comprehensive practice questions and preparation strategies. Cutting-Edge ML Trends: Stay updated with the latest advancements in Google Cloud machine learning technologies, ensuring your skills remain relevant and innovative. Authored by a Machine Learning Expert Written by an experienced practitioner in the field of machine learning on Google Cloud, this guide bridges the gap between theoretical knowledge and practical application, offering a rich and comprehensive learning experience. Your Comprehensive Guide to ML Certification Whether you’re an experienced machine learning engineer or looking to elevate your expertise in Google Cloud's ML offerings, this book is a valuable companion, guiding you through the intricacies of machine learning in Google Cloud and preparing you for the Professional Machine Learning Engineer certification. Elevate Your Machine Learning Journey This guide is more than a pathway to certification; it's a deep dive into the practical and innovative aspects of machine learning in the Google Cloud environment, designed to equip you with the skills and knowledge for a thriving career in this dynamic field. Begin Your Machine Learning Adventure Start your journey to becoming a certified Google Professional Machine Learning Engineer. This guide is not just about passing an exam; it's about unlocking new opportunities and frontiers in the exciting world of machine learning on Google Cloud. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Building Machine Learning and Deep Learning Models on Google Cloud Platform

Ekaba Bisong

Author : Ekaba Bisong
Publisher : Apress
Page : 703 pages
File Size : 47,5 Mb
Release : 2019-09-27
Category : Computers
ISBN : 9781484244708

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Building Machine Learning and Deep Learning Models on Google Cloud Platform by Ekaba Bisong Pdf

Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

Learning Google Cloud Vertex AI

Hemanth Kumar K

Author : Hemanth Kumar K
Publisher : BPB Publications
Page : 308 pages
File Size : 48,9 Mb
Release : 2023-08-28
Category : Computers
ISBN : 9789355515353

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Learning Google Cloud Vertex AI by Hemanth Kumar K Pdf

Learn how to build an end-to-end data to AI solution on Google Cloud using Vertex AI KEY FEATURES ● Harness the power of AutoML capabilities to build machine learning models. ● Learn how to train custom machine learning models on the Google Cloud Platform. ● Accelerate your career in data analytics by leveraging the capabilities of GCP. DESCRIPTION Google Cloud Vertex AI is a platform for machine learning (ML) offered by Google Cloud, with the objective of making the creation, deployment, and administration of ML models on a large scale easier. If you are seeking a unified and collaborative environment for your ML projects, this book is a valuable resource for you. This comprehensive guide is designed to help data enthusiasts effectively utilize Google Cloud Platform's Vertex AI for a wide range of machine learning operations. It covers the basics of the Google Cloud Platform, encompassing cloud storage, big query, and IAM. Subsequently, it delves into the specifics of Vertex AI, including AutoML, custom model training, model deployment on endpoints, development of Vertex AI pipelines, and the Explainable AI feature store. By the time you finish reading this book, you will be able to navigate Vertex AI proficiently, even if you lack prior experience with cloud platforms. With the inclusion of numerous code examples throughout the book, you will be equipped with the necessary skills and confidence to create machine learning solutions using Vertex AI. WHAT YOU WILL LEARN ● Learn how to create projects, store data in GCP, and manage access permissions effectively. ● Discover how AutoML can be utilized for streamlining workflows. ● Learn how to construct pipelines using TFX (TensorFlow Extended) and Kubeflow components. ● Gain an overview of the purpose and significance of the Feature Store. ● Explore the concept of explainable AI and its role in understanding machine learning models. WHO THIS BOOK IS FOR This book is designed for data scientists and advanced AI practitioners who are interested in learning how to perform machine learning tasks on the Google Cloud Platform. Having prior knowledge of machine learning concepts and proficiency in Python programming would greatly benefit readers. TABLE OF CONTENTS 1. Basics of Google Cloud Platform 2. Introduction to Vertex AI and AutoML Tabular 3. AutoML Image, Text, and Pre-built Models 4. Vertex AI Workbench and Custom Model Training 5. Vertex AI Custom Model Hyperparameter and Deployment 6. Introduction to Pipelines and Kubeflow 7. Pipelines using Kubeflow for Custom Models 8. Pipelines using TensorFlow Extended 9. Vertex AI Feature Store 10. Explainable AI

Journey to Become a Google Cloud Machine Learning Engineer

Dr. Logan Song

Author : Dr. Logan Song
Publisher : Packt Publishing Ltd
Page : 330 pages
File Size : 44,9 Mb
Release : 2022-09-20
Category : Computers
ISBN : 9781803239415

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Journey to Become a Google Cloud Machine Learning Engineer by Dr. Logan Song Pdf

Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills Key FeaturesA comprehensive yet easy-to-follow Google Cloud machine learning study guideExplore full-spectrum and step-by-step practice examples to develop hands-on skillsRead through and learn from in-depth discussions of Google ML certification exam questionsBook Description This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate. What you will learnProvision Google Cloud services related to data science and machine learningProgram with the Python programming language and data science librariesUnderstand machine learning concepts and model development processesExplore deep learning concepts and neural networksBuild, train, and deploy ML models with Google BigQuery ML, Keras, and Google Cloud Vertex AIDiscover the Google Cloud ML Application Programming Interface (API)Prepare to achieve Google Cloud Professional Machine Learning Engineer certificationWho this book is for Anyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book.

Hands-On Serverless Computing

Kuldeep Chowhan

Author : Kuldeep Chowhan
Publisher : Packt Publishing Ltd
Page : 339 pages
File Size : 54,5 Mb
Release : 2018-07-23
Category : Computers
ISBN : 9781788834957

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Hands-On Serverless Computing by Kuldeep Chowhan Pdf

Deploy functions efficiently using different cloud-based serverless offerings Key Features Understand the concept of Function-as-a-Service Implement Serverless solutions using AWS Lambda, Azure Functions and Google Cloud Functions Practical approach towards choosing the best tool for your serverless environment Book Description Serverless applications and architectures are gaining momentum and are increasingly being used by companies of all sizes. Serverless software takes care of many problems that developers face when running systems and servers, such as fault tolerance, centralized logging, horizontal scalability, and deployments. You will learn how to harness serverless technology to rapidly reduce production time and minimize your costs, while still having the freedom to customize your code, without hindering functionality. Upon finishing the book, you will have the knowledge and resources to build your own serverless application hosted in AWS, Microsoft Azure, or Google Cloud Platform, and will have experienced the benefits of event-driven technology for yourself. This hands-on guide dives into the basis of serverless architectures and how to build them using Node.js as a programming language, Visual Studio Code for code editing, and Postman for quickly and securely developing applications without the hassle of configuring and maintaining infrastructure on three public cloud platforms. What you will learn Understand the benefts of serverless computing and know when to use it Develop serverless applications on AWS, Azure, and Google Cloud Get to grips with Function as a Service (FaaS) Apply triggers to serverless functions Build event-driven apps using serverless frameworks Use the Node.js programming language to build serverless apps Use code editors, such as Visual Studio Code, as development environments Master the best development practices for creating scalable and practical solutions Who this book is for This book is targeted towards developers, system administrators or any stakeholder working in the Serverless environment and want to understand how functions work. Basic idea of serverless architecture can be an added advantage

Learning Google Bigquery

Learning Google BigQuery by Eric Brown,Thirukkumaran Haridass Pdf

Google BigQuery: The Definitive Guide by Valliappa Lakshmanan,Jordan Tigani Pdf

Learning Google BigQuery by Eric Brown,Thirukkumaran Haridass Pdf

Machine Learning with BigQuery ML by Alessandro Marrandino Pdf

Google Bigquery by Valliappa Lakshmanan,Jordan Tigani Pdf

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