Cloud computing has caused a marketing fog, confusing business executives seeking to understand the technology's potential applications and business benefits. A Quick-Start Guide to Cloud Computing cuts through the industry hype and provides non-technical explanations about what it is and how it can improve your business. With case studies from large and small business, it shows how enabling a remote workforce and sharing resources can reduce your organisation's carbon footprint. It describes: the benefits of cloud computing; how to choose the right supplier and technologies for your particular business; key security issues and the perils and pitfalls to avoid. This Quick Start Guide puts business needs before technology, enabling you to make confident decisions about IT strategy, make the right choices for your business and reject 'solutions' that fix problems you don't have.
Leverage the power of various Google Cloud AI Services by building a smart web application using MEAN Stack Key Features Start working with the Google Cloud Platform and the AI services it offers Build smart web applications by combining the power of Google Cloud AI services and the MEAN stack Build a web-based dashboard of smart applications that perform language processing, translation, and computer vision on the cloud Book Description Cognitive services are the new way of adding intelligence to applications and services. Now we can use Artificial Intelligence as a service that can be consumed by any application or other service, to add smartness and make the end result more practical and useful. Google Cloud AI enables you to consume Artificial Intelligence within your applications, from a REST API. Text, video and speech analysis are among the powerful machine learning features that can be used. This book is the easiest way to get started with the Google Cloud AI services suite and open up the world of smarter applications. This book will help you build a Smart Exchange, a forum application that will let you upload videos, images and perform text to speech conversions and translation services. You will use the power of Google Cloud AI Services to make our simple forum application smart by validating the images, videos, and text provided by users to Google Cloud AI Services and make sure the content which is uploaded follows the forum standards, without a human curator involvement. You will learn how to work with the Vision API, Video Intelligence API, Speech Recognition API, Cloud Language Process, and Cloud Translation API services to make your application smarter. By the end of this book, you will have a strong understanding of working with Google Cloud AI Services, and be well on the way to building smarter applications. What you will learn Understand Google Cloud Platform and its Cloud AI services Explore the Google ML Services Work with an Angular 5 MEAN stack application Integrate Vision API, Video Intelligence API for computer vision Be ready for conversational experiences with the Speech Recognition API, Cloud Language Process and Cloud Translation API services Build a smart web application that uses the power of Google Cloud AI services to make apps smarter Who this book is for This book is ideal for data professionals and web developers who want to use the power of Google Cloud AI services in their projects, without the going through the pain of mastering machine learning for images, videos and text. Some familiarity with the Google Cloud Platform will be helpful.
Cloud computing saves time and money by enabling convenient, on-demand network access to resources like servers and applications. Libraries that take advantage of the cloud have fewer IT problems because data centers provide continuous updates and mobility that standard computing cannot easily provide.
Data in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytes—or over 50 million gigabytes—of genomic data, and they’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 O’Connor of the UC Santa Cruz Genomics Institute, guide you through the process. You’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
Discover techniques and tools for building serverless applications with AWS Lambda Key Features Learn to write, run, and deploy Lambda functions in the AWS cloud Make the most of AWS Lambda functions to build scalable and cost-efficient systems A practical guide to developing serverless services and applications in Node.js, Java, Python, and C# Book Description AWS Lambda is a part of AWS that lets you run your code without provisioning or managing servers. This enables you to deploy applications and backend services that operate with no upfront cost. This book gets you up to speed on how to build scalable systems and deploy serverless applications with AWS Lambda. The book starts with the fundamental concepts of AWS Lambda, and then teaches you how to combine your applications with other AWS services, such as AmazonAPI Gateway and DynamoDB. This book will also give a quick walk through on how to use the Serverless Framework to build larger applications that can structure code or autogenerate boilerplate code that can be used to get started quickly for increased productivity. Toward the end of the book, you will learn how to write, run, and test Lambda functions using Node.js, Java, Python, and C#. What you will learn Understand the fundamental concepts of AWS Lambda Get to grips with the Serverless Framework and how to create a serverless project Testing and debugging Lambda functions Create a stateful, serverless backend with DynamoDB Program AWS Lambda with Java, Python, and C# Program a lambda function with Node.js Who this book is for This book is primarily for IT architects and developers who want to build scalable systems and deploy serverless applications with AWS Lambda. No prior knowledge of AWS is necessary.
Everything you wanted to know about cloud computing, but were afraid to ask:What is cloud computing… really?What's the least I need to know?How will it affect me?
Web Scraping techniques are getting more popular, since data is as valuable as oil in 21st century. Through this book get some key knowledge about using XPath, regEX; web scraping libraries for R like rvest and RSelenium technologies. Key FeaturesTechniques, tools and frameworks for web scraping with RScrape data effortlessly from a variety of websites Learn how to selectively choose the data to scrape, and build your datasetBook Description Web scraping is a technique to extract data from websites. It simulates the behavior of a website user to turn the website itself into a web service to retrieve or introduce new data. This book gives you all you need to get started with scraping web pages using R programming. You will learn about the rules of RegEx and Xpath, key components for scraping website data. We will show you web scraping techniques, methodologies, and frameworks. With this book's guidance, you will become comfortable with the tools to write and test RegEx and XPath rules. We will focus on examples of dynamic websites for scraping data and how to implement the techniques learned. You will learn how to collect URLs and then create XPath rules for your first web scraping script using rvest library. From the data you collect, you will be able to calculate the statistics and create R plots to visualize them. Finally, you will discover how to use Selenium drivers with R for more sophisticated scraping. You will create AWS instances and use R to connect a PostgreSQL database hosted on AWS. By the end of the book, you will be sufficiently confident to create end-to-end web scraping systems using R. What you will learnWrite and create regEX rulesWrite XPath rules to query your dataLearn how web scraping methods workUse rvest to crawl web pagesStore data retrieved from the webLearn the key uses of Rselenium to scrape dataWho this book is for This book is for R programmers who want to get started quickly with web scraping, as well as data analysts who want to learn scraping using R. Basic knowledge of R is all you need to get started with this book.
Explore IoT Architecture, Design, and its Implementation KEY FEATURES ● Comprehensive overview of frameworks, protocols, networks, security, and privacy of IoT. ● Covers innovative IoT use cases and industry-wide application areas. ● Includes case studies to demonstrate IoT principles and practices. DESCRIPTION Internet of Things (IoT) A Quick Start Guide explains the architecture, design, and implementation of IoT. The book charts a path where none exists and introduces readers to the ethical and responsible development of IoT solutions. The book begins with the history of IoT, followed by chapters on architectures, networks, and protocols in both software and hardware. The book reveals the next level of IoT framework knowledge, such as ThingWorx and Salesforce Thunder. This book places equal emphasis on a wide range of security and privacy aspects, including Zero Trust Approaches, Forensics, Access Control Lists, and Public Key Infrastructure. Wearables, Industry 4.0, Workplace Analytics, and Product Asset Management are just a few of the applications and use cases that are discussed. Transformative trends such as Augmented Analytics, AR/VR, Digital Twins, and many more are also discussed in the book. After reading this book, readers will get a broad spectrum of knowledge of IoT. They will be able to put the guidance shared to use. WHAT YOU WILL LEARN ● Access to a variety of IoT application areas with compelling use cases. ● Opportunity to experiment with frameworks, tools, and platforms for various IoT assignments. ● Acquire conceptual knowledge about IoT architecture, protocols, and networks. ● Take a look at integrating IoT procedures, software, and hardware. ● Investigate how to develop a data management strategy when implementing IoT. ● Understand the policies governing IoT security, privacy, and interoperability. WHO THIS BOOK IS FOR This book is intended for IT graduates, computer engineers, and industry experts who wish to learn IoT principles, techniques, and protocols to successfully create and deploy safe and secure IoT systems. One does not need prior knowledge of IoT or programming to read this book. TABLE OF CONTENTS 1. IoT: The Basic Dynamics 2. IoT—Nuts and Bolts of the Architecture 3. Data Management Strategy 4. IoT Security, Privacy and Interoperability: What, Why, How, and What Next 5. Applications and Use Cases 6. Current and Future Trends
This book describes the landscape of cloud computing from first principles, leading the reader step-by-step through the process of building and configuring a cloud environment. The book not only considers the technologies for designing and creating cloud computing platforms, but also the business models and frameworks in real-world implementation of cloud platforms. Emphasis is placed on “learning by doing,” and readers are encouraged to experiment with a range of different tools and approaches. Topics and features: includes review questions, hands-on exercises, study activities and discussion topics throughout the text; demonstrates the approaches used to build cloud computing infrastructures; reviews the social, economic, and political aspects of the on-going growth in cloud computing use; discusses legal and security concerns in cloud computing; examines techniques for the appraisal of financial investment into cloud computing; identifies areas for further research within this rapidly-moving field.
Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Key FeaturesMake a hands-on start in the fields of Big Data, Distributed Technologies and Machine LearningLearn how to design, develop and interpret the results of common Machine Learning algorithmsUncover hidden patterns in your data in order to derive real actionable insights and business valueBook Description Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data. What you will learnUnderstand how Spark fits in the context of the big data ecosystemUnderstand how to deploy and configure a local development environment using Apache SparkUnderstand how to design supervised and unsupervised learning modelsBuild models to perform NLP, deep learning, and cognitive services using Spark ML librariesDesign real-time machine learning pipelines in Apache SparkBecome familiar with advanced techniques for processing a large volume of data by applying machine learning algorithmsWho this book is for This book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.
