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Zero Trust Networks
The perimeter defenses guarding your network perhaps are not as secure as you think. Hosts behind the firewall have no defenses of their own, so when a host in the "trusted" zone is breached, access to your data center is not far behind. That’s an all-too-familiar scenario today. With this practical book, you’ll learn the principles behind zero trust architecture, along with details necessary to implement it. The Zero Trust Model treats all hosts as if they’re internet-facing, and considers the entire network to be compromised and hostile. By taking this approach, you’ll focus on building strong authentication, authorization, and encryption throughout, while providing compartmentalized access and better operational agility. Understand how perimeter-based defenses have evolved to become the broken model we use today Explore two case studies of zero trust in production networks on the client side (Google) and on the server side (PagerDuty) Get example configuration for open source tools that you can use to build a zero trust network Learn how to migrate from a perimeter-based network to a zero trust network in production
R for Data Science
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Information Architecture for the World Wide Web
Today's web sites and intranets are larger, more valuable, and more complex than ever before, and their users are busier and less forgiving. Designers, information architects, and web site managers are required to juggle vast amounts of information, frequent changes, new technologies, and corporate politics, making some web sites look like a fast-growing but poorly planned city -roads everywhere, but impossible to navigate. A well-planned information architecture has never been as essential as it is now. Information Architecture for the World Wide Web, Second Edition, shows how to use both aesthetics and mechanics to create distinctive, cohesive web sites that work. Most books on web develop...
HTTP: The Definitive Guide
This guide gives a complete and detailed description of the HTTP protocol and how it shapes the landscape of the Web by the technologies that it supports.
Introducing MLOps
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cyc...
Infrastructure as Code
Six years ago, Infrastructure as Code was a new concept. Today, as even banks and other conservative organizations plan moves to the cloud, development teams for companies worldwide are attempting to build large infrastructure codebases. With this practical book, Kief Morris of ThoughtWorks shows you how to effectively use principles, practices, and patterns pioneered by DevOps teams to manage cloud-age infrastructure. Ideal for system administrators, infrastructure engineers, software developers, team leads, and architects, this updated edition demonstrates how you can exploit cloud and automation technology to make changes easily, safely, quickly, and responsibly. You'll learn how to defin...
Learning the Vi Editor
For many users, working in the Unix environment means usingvi, a full-screen text editor available on most Unix systems. Even those who knowvioften make use of only a small number of its features. Learning the vi Editoris a complete guide to text editing withvi. Topics new to the sixth edition include multiscreen editing and coverage of fourviclones:vim,elvis,nvi, andvileand their enhancements tovi, such as multi-window editing, GUI interfaces, extended regular expressions, and enhancements for programmers. A new appendix describesvi's place in the Unix and Internet cultures. Quickly learn the basics of editing, cursor movement, and global search and replacement. Then take advantage of the m...
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help yo...
Data Science from Scratch
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-h...
Big Data Now: Current Perspectives from O'Reilly Radar
This collection represents the full spectrum of data-related content we’ve published on O’Reilly Radar over the last year. Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas: Data issues -- The opportunities and ambiguities of the data space are evident in discussions around privacy, the implications of data-centric industries, and the debate about the phrase “data science” itself. The application of data: products and processes – A “data product” can emerge ...
