Google Cloud Platform in Action PDF Download

Author: John J. (JJ) Geewax
Publisher: Simon and Schuster
ISBN: 1638355908
Category : Computers
Languages : en
Pages : 920

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Book Description
Summary Google Cloud Platform in Action teaches you to build and launch applications that scale, leveraging the many services on GCP to move faster than ever. You'll learn how to choose exactly the services that best suit your needs, and you'll be able to build applications that run on Google Cloud Platform and start more quickly, suffer fewer disasters, and require less maintenance. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Thousands of developers worldwide trust Google Cloud Platform, and for good reason. With GCP, you can host your applications on the same infrastructure that powers Search, Maps, and the other Google tools you use daily. You get rock-solid reliability, an incredible array of prebuilt services, and a cost-effective, pay-only-for-what-you-use model. This book gets you started. About the Book Google Cloud Platform in Action teaches you how to deploy scalable cloud applications on GCP. Author and Google software engineer JJ Geewax is your guide as you try everything from hosting a simple WordPress web app to commanding cloud-based AI services for computer vision and natural language processing. Along the way, you'll discover how to maximize cloud-based data storage, roll out serverless applications with Cloud Functions, and manage containers with Kubernetes. Broad, deep, and complete, this authoritative book has everything you need. What's inside The many varieties of cloud storage and computing How to make cost-effective choices Hands-on code examples Cloud-based machine learning About the Reader Written for intermediate developers. No prior cloud or GCP experience required. About the Author JJ Geewax is a software engineer at Google, focusing on Google Cloud Platform and API design. Table of Contents PART 1 - GETTING STARTED What is "cloud"? Trying it out: deploying WordPress on Google Cloud The cloud data center PART 2 - STORAGE Cloud SQL: managed relational storage Cloud Datastore: document storage Cloud Spanner: large-scale SQL Cloud Bigtable: large-scale structured data Cloud Storage: object storage PART 3 - COMPUTING Compute Engine: virtual machines Kubernetes Engine: managed Kubernetes clusters App Engine: fully managed applications Cloud Functions: serverless applications Cloud DNS: managed DNS hosting PART 4 - MACHINE LEARNING Cloud Vision: image recognition Cloud Natural Language: text analysis Cloud Speech: audio-to-text conversion Cloud Translation: multilanguage machine translation Cloud Machine Learning Engine: managed machine learning PART 5 - DATA PROCESSING AND ANALYTICS BigQuery: highly scalable data warehouse Cloud Dataflow: large-scale data processing Cloud Pub/Sub: managed event publishing

Author: Ted Hunter
Publisher: Packt Publishing Ltd
ISBN: 1788830830
Category : Computers
Languages : en
Pages : 496

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Book Description
Develop, deploy, and scale your applications with Google Cloud Platform Key Features Create and deploy your applications on Google Cloud Platform Store and manage source code and debug Cloud-hosted apps with plugins and IDEs Streamline developer workflows with tools for alerting and managing deployments Book Description Google Cloud Platform (GCP) provides autoscaling compute power and distributed in-memory cache, task queues, and datastores to write, build, and deploy Cloud-hosted applications. With Google Cloud Platform for Developers, you will be able to develop and deploy scalable applications from scratch and make them globally available in almost any language. This book will guide you in designing, deploying, and managing applications running on Google Cloud. You’ll start with App Engine and move on to work with Container Engine, compute engine, and cloud functions. You’ll learn how to integrate your new applications with the various data solutions on GCP, including Cloud SQL, Bigtable, and Cloud Storage. This book will teach you how to streamline your workflow with tools such as Source Repositories, Container Builder, and StackDriver. Along the way, you’ll see how to deploy and debug services with IntelliJ, implement continuous delivery pipelines, and configure robust monitoring and alerting for your production systems. By the end of this book, you’ll be well-versed with all the development tools of Google Cloud Platform, and you’ll develop, deploy, and manage highly scalable and reliable applications. What you will learn Understand the various service offerings on GCP Deploy and run services on managed platforms such as App Engine and Container Engine Securely maintain application states with Cloud Storage, Datastore, and Bigtable Leverage StackDriver monitoring and debugging to minimize downtime and mitigate issues without impacting users Design and implement complex software solutions utilizing Google Cloud Integrate with best-in-class big data solutions such as Bigquery, Dataflow, and Pub/Sub Who this book is for Google Cloud Platform for Developers is for application developers. This book will enable you to fully leverage the power of Google Cloud Platform to build resilient and intelligent software solutions.

Author: Stephen Orban
Publisher: Createspace Independent Publishing Platform
ISBN: 9781981924318
Category :
Languages : en
Pages : 334

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Book Description
Cloud computing is the most significant technology development of our lifetimes. It has made countless new businesses possible and presents a massive opportunity for large enterprises to innovate like startups and retire decades of technical debt. But making the most of the cloud requires much more from enterprises than just a technology change. Stephen Orban led Dow Jones's journey toward digital agility as their CIO and now leads AWS's Enterprise Strategy function, where he helps leaders from the largest companies in the world transform their businesses. As he demonstrates in this book, enterprises must re-train their people, evolve their processes, and transform their cultures as they move to the cloud. By bringing together his experiences and those of a number of business leaders, Orban shines a light on what works, what doesn't, and how enterprises can transform themselves using the cloud.

Author: Klaus Elk
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 1547401044
Category : Computers
Languages : en
Pages : 367

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Book Description
With a mixture of theory, examples, and well-integrated figures, Embedded Software for the IoT helps the reader understand the details in the technologies behind the devices used in the Internet of Things. It provides an overview of IoT, parameters of designing an embedded system, and good practice concerning code, version control and defect-tracking needed to build and maintain a connected embedded system. After presenting a discussion on the history of the internet and the word wide web the book introduces modern CPUs and operating systems. The author then delves into an in-depth view of core IoT domains including: Wired and wireless networking Digital filters Security in embedded and networked systems Statistical Process Control for Industry 4.0 This book will benefit software developers moving into the embedded realm as well as developers already working with embedded systems.

Author: Noah Gift
Publisher: Addison-Wesley Professional
ISBN: 0134863917
Category : Computers
Languages : en
Pages : 720

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Book Description
Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Author: Geraldine A. Van der Auwera
Publisher: O'Reilly Media
ISBN: 1491975164
Category : Science
Languages : en
Pages : 496

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Book Description
Data in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytesâ??or over 50 million gigabytesâ??of genomic data, and theyâ??re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud? With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian Oâ??Connor of the UC Santa Cruz Genomics Institute, guide you through the process. Youâ??ll learn by working with real data and genomics algorithms from the field. This book covers: Essential genomics and computing technology background Basic cloud computing operations Getting started with GATK, plus three major GATK Best Practices pipelines Automating analysis with scripted workflows using WDL and Cromwell Scaling up workflow execution in the cloud, including parallelization and cost optimization Interactive analysis in the cloud using Jupyter notebooks Secure collaboration and computational reproducibility using Terra

Author: Ekaba Bisong
Publisher: Apress
ISBN: 1484244702
Category : Computers
Languages : en
Pages : 703

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Book Description
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

Author: Ted Hunter
Publisher: Packt Publishing Ltd
ISBN: 1838648704
Category : Computers
Languages : en
Pages : 763

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Book Description
Build cost-effective and robust cloud solutions with Google Cloud Platform (GCP) using these simple and practical recipes Key FeaturesExplore the various service offerings of the GCPHost a Python application on Google Compute EngineSecurely maintain application states with Cloud Storage, Datastore, and BigtableBook Description GCP is a cloud computing platform with a wide range of products and services that enable you to build and deploy cloud-hosted applications. This Learning Path will guide you in using GCP and designing, deploying, and managing applications on Google Cloud. You will get started by learning how to use App Engine to access Google's scalable hosting and build software that runs on this framework. With the help of Google Compute Engine, you’ll be able to host your workload on virtual machine instances. The later chapters will help you to explore ways to implement authentication and security, Cloud APIs, and command-line and deployment management. As you hone your skills, you’ll understand how to integrate your new applications with various data solutions on GCP, including Cloud SQL, Bigtable, and Cloud Storage. Following this, the book will teach you how to streamline your workflow with tools, including Source Repositories, Container Builder, and Stackdriver. You'll also understand how to deploy and debug services with IntelliJ, implement continuous delivery pipelines, and configure robust monitoring and alerts for your production systems. By the end of this Learning Path, you'll be well versed with GCP’s development tools and be able to develop, deploy, and manage highly scalable and reliable applications. This Learning Path includes content from the following Packt products: Google Cloud Platform for Developers Ted Hunter and Steven PorterGoogle Cloud Platform Cookbook by Legorie Rajan PSWhat you will learnHost an application using Google Cloud FunctionsMigrate a MySQL database to Cloud SpannerConfigure a network for a highly available application on GCPLearn simple image processing using Storage and Cloud FunctionsAutomate security checks using Policy ScannerDeploy and run services on App Engine and Container EngineMinimize downtime and mitigate issues with Stackdriver Monitoring and DebuggerIntegrate with big data solutions, including BigQuery, Dataflow, and Pub/SubWho this book is for This Learning Path is for IT professionals, engineers, and developers who want to implement Google Cloud in their organizations. Administrators and architects planning to make their organization more efficient with Google Cloud will also find this Learning Path useful. Basic understanding of GCP and its services is a must.

Author: Adi Wijaya
Publisher: Packt Publishing Ltd
ISBN: 1800565062
Category : Computers
Languages : en
Pages : 440

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Book Description
Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.

Author: Valliappa Lakshmanan
Publisher: "O'Reilly Media, Inc."
ISBN: 1491974532
Category : Computers
Languages : en
Pages : 403

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Book Description
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You’ll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines