Is Kubernetes ready for stateful workloads? This open source system has become the primary platform for deploying and managing cloud native applications. But because it was originally designed for stateless workloads, working with data on Kubernetes has been challenging. If you want to avoid the inefficiencies and duplicative costs of having separate infrastructure for applications and data, this practical guide can help.Using Kubernetes as your platform, you'll learn open source technologies that are designed and built for the cloud. Authors Jeff Carpenter and Patrick McFadin provide case studies to help you explore new use cases and avoid the pitfalls others have faced. Youll get an insider's view of what's coming from innovators who are creating next-generation architectures and infrastructure.With this book, you will:Learn how to use basic Kubernetes resources to compose data infrastructureAutomate the deployment and operations of data infrastructure on Kubernetes using tools like Helm and operatorsEvaluate and select data infrastructure technologies for use in your applicationsIntegrate data infrastructure technologies into your overall stackExplore emerging technologies that will enhance your Kubernetes-based applications in the future
The low cost of getting started with cloud services can easily evolve into a significant expense down the road. That's challenging for teams developing data pipelines, particularly when rapid changes in technology and workload require a constant cycle of redesign. How do you deliver scalable, highly available products while keeping costs in check? With this practical guide, author Sev Leonard provides a holistic approach to designing scalable data pipelines in the cloud. Intermediate data engineers, software developers, and architects will learn how to navigate cost/performance trade-offs and how to choose and configure compute and storage. You'll also pick up best practices for code development, testing, and monitoring. By focusing on the entire design process, you'll be able to deliver cost-effective, high-quality products. This book helps you: Reduce cloud spend with lower cost cloud service offerings and smart design strategies Minimize waste without sacrificing performance by rightsizing compute resources Drive pipeline evolution, head off performance issues, and quickly debug with effective monitoring Set up development and test environments that minimize cloud service dependencies Create data pipeline code bases that are testable and extensible, fostering rapid development and evolution Improve data quality and pipeline operation through validation and testing
"Learn the core concepts of Vue.js, the modern JavaScript framework for building frontend applications and interfaces from scratch. With concise, practical, and clear examples, this book takes web developers step-by-step through the tools and libraries in the Vue.js ecosystem and shows them how to create complete applications for real-world web projects. You'll learn how to handle data communication between components with Pinia architecture, develop a manageable routing system for a frontend project to control the application flow, and produce basic animation effects to create a better user experience"--
After decades of relative obscurity, functional programming is finally coming into its own. With concise, easy-to-read code that supports asynchronous, concurrent processing, aspects of functional programming have begun to appear in several traditionally object-oriented languages such as C# and Java. This practical book shows C# programmers how to use functional programming features without having to navigate an entirely new language. Because of the shared runtime environment common to C# and F# languages, it's possible to use most of F#'s functional features in C# as well. Author Simon J. Painter explains how you can write functional code in C# right away, without having to install dependencies or features newer than .NET 3. You'll learn why functional programming concepts can bring immediate benefit to your work. Learn what functional programming is and how it originated Discover features of the functional paradigm using a more familiar language Start coding functionally in C# right away, without relying on third-party libraries Write code that's more robust, less error prone, and easier to test Examine less conventional ways to look at structures available in C# Explore the practicalities of using functional C# in a business environment.
Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help. Authors Ethan Cowan, Michael Shoemate, and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows. With this book, you'll learn: How DP guarantees privacy when other data anonymization methods don't What preserving individual privacy in a dataset entails How to apply DP in several real-world scenarios and datasets Potential privacy attack methods, including what it means to perform a reidentification attack How to use the OpenDP library in privacy-preserving data releases How to interpret guarantees provided by specific DP data releases
If your organization plans to modernize services and move to the cloud from legacy software or a private cloud on premises, this book is for you. Software developers, solution architects, cloud engineers, and anybody interested in cloud technologies will learn fundamental concepts for cloud computing, migration, transformation, and development using Microsoft Azure. Author and Microsoft MVP Jonah Carrio Andersson guides you through cloud computing concepts and deployment models, the wide range of modern cloud technologies, application development with Azure, team collaboration services, security services, and cloud migration options in Microsoft Azure. You'll gain insight into the Microsoft Azure cloud services that you can apply in different business use cases, software development projects, and modern solutions in the cloud. You'll also become fluent with Azure cloud migration services, serverless computing technologies that help your development team work productively, Azure IoT, and Azure cognitive services that make your application smarter. This book also provides real-world advice and best practices based on the author's own Azure migration experience.
"As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. [This] is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the 'technical/nontechnical' divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas"--
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 "e;trusted"e; zone is breached, access to your data center is not far behind. Thats an all-too-familiar scenario today. With this practical book, youll learn the principles behind zero trust architecture, along with details necessary to implement it.The Zero Trust Model treats all hosts as if theyre internet-facing, and considers the entire network to be compromised and hostile. By taking this approach, youll 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 todayExplore 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 networkLearn how to migrate from a perimeter-based network to a zero trust network in production
"When Isaac Newton developed calculus in the 1600s, he was trying to tie together math and physics in an intuitive, geometrical way. But over time math and physics teaching became heavily weighted toward algebra, and less toward geometrical problem solving. However, many practicing mathematicians and physicists will get their intuition geometrically first and do the algebra later. Make:Calculus imagines how Newton might have used 3D printed models, construction toys, programming, craft materials, and an Arduino or two to teach calculus concepts in an intuitive way. The book uses as little reliance on algebra as possible while still retaining enough to allow comparison with a traditional curriculum. This book is not a traditional Calculus I textbook. Rather, it will take the reader on a tour of key concepts in calculus that lend themselves to hands-on projects. This book also defines terms and common symbols for them so that self-learners can learn more on their own"
Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to embedded Linux devices. This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started. Develop your expertise in AI and ML for edge devices Understand which projects are best solved with edge AI Explore key design patterns for edge AI apps Learn an iterative workflow for developing AI systems Build a team with the skills to solve real-world problems Follow a responsible AI process to create effective products
Why are so many companies adopting GitOps for their DevOps and cloud native strategy? This reliable framework is quickly becoming the standard method for deploying apps to Kubernetes. With this practical, developer-oriented book, DevOps engineers, developers, IT architects, and SREs will learn the most useful recipes and examples for following GitOps practices.
The software development ecosystem is constantly changing, providing a constant stream of new tools, frameworks, techniques, and paradigms. Over the past few years, incremental developments in core engineering practices for software development have created the foundations for rethinking how architecture changes over time, along with ways to protect important architectural characteristics as it evolves. This practical guide ties those parts together with a new way to think about architecture and time.
While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment, or predict early failures in rotating machinery? This practical book teaches you the skills necessary to put AI and machine learning to work at your company.Applied Machine Learning and AI for Engineers provides examples and illustrations from the AI and ML course Prosise teaches at companies and research institutions worldwide. There's no fluff and no scary equationsjust a fast start for engineers and software developers, complete with hands-on examples.This book helps you:Learn what machine learning and deep learning are and what they can accomplishUnderstand how popular learning algorithms work and when to apply themBuild machine learning models in Python with Scikit-Learn, and neural networks with Keras and TensorFlowTrain and score regression models and binary and multiclass classification modelsBuild facial recognition models and object detection modelsBuild language models that respond to natural-language queries and translate text to other languagesUse Cognitive Services to infuse AI into the apps that you write
Expert advice on C programming is hard to find. While much help is available for object-oriented programming languages, there's surprisingly little for the C language. With this hands-on guide, beginners and experienced C programmers alike will find guidance about design decisions, including how to apply them bit by bit to running code examples when building large-scale programs.Christopher Preschern, a leading member of the design patterns community, answers questions such as how to structure C programs, cope with error handling, or design flexible interfaces. Whether you're looking for one particular pattern or an overview of design options for a specific topic, this book shows you how to implement hands-on design knowledge specifically for the C programming language.You'll find design patterns for:Error handlingReturning error informationMemory managementReturning data from C functionsData lifetime and ownershipFlexible APIsFlexible iterator interfacesOrganizing files in modular programsEscaping #ifdef Hell
Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve.Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answeringLearn how transformers can be used for cross-lingual transfer learningApply transformers in real-world scenarios where labeled data is scarceMake transformer models efficient for deployment using techniques such as distillation, pruning, and quantizationTrain transformers from scratch and learn how to scale to multiple GPUs and distributed environments
Candlesticks have become a key component of platforms and charting programs for financial trading. With these charts, traders can learn underlying patterns for interpreting price action history and forecasts. This A-Z guide shows portfolio managers, quants, strategists, and analysts how to use Python to recognize, scan, trade, and back-test the profitability of candlestick patterns.Financial author, trading consultant, and institutional market strategist Sofien Kaabar shows you how to create a candlestick scanner and indicator so you can compare the profitability of these patterns. With this hands-on book, you'll also explore a new type of charting system similar to candlesticks, as well as new patterns that have never been presented before.With this book, you will:Create and understand the conditions required for classic and modern candlestick patternsLearn the market psychology behind themUse a framework to learn how back-testing trading strategies are conductedExplore different charting systems and understand their limitationsImport OHLC historical FX data in Python in different time framesUse algorithms to scan for and reproduce patternsLearn a pattern's potential by evaluating its profitability and predictability
Author Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance.
Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow.This essential book provides:A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needsTips and best practices for implementing these techniquesA guide to interacting with explainability and how to avoid common pitfallsThe knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systemsAdvice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text dataExample implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
Microsoft Power BI is a data analytics and visualization tool powerful enough for the most demanding data scientists, but accessible enough for everyday use for anyone who needs to get more from data. The market has many books designed to train and equip professional data analysts to use Power BI, but few of them make this tool accessible to anyone who wants to get up to speed on their own.This streamlined intro to Power BI covers all the foundational aspects and features you need to go from "e;zero to hero"e; with data and visualizations. Whether you work with large, complex datasets or work in Microsoft Excel, author Jeremey Arnold shows you how to teach yourself Power BI and use it confidently as a regular data analysis and reporting tool.You'll learn how to:Import, manipulate, visualize, and investigate data in Power BIApproach solutions for both self-service and enterprise BIUse Power BI in your organization's business intelligence strategyProduce effective reports and dashboardsCreate environments for sharing reports and managing data access with your teamDetermine the right solution for using Power BI offerings based on size, security, and computational needs
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 activationlinking 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 GA4The potential use cases that GA4 integrations can enableSkills and resources needed to create GA4 integrationsHow much GA4 data capture is necessary to enable use casesThe process of designing dataflows from strategy though data storage, modeling, and activation
Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions.
If you're interested in applying FP concepts to your Java code, this book is for you. You'll learn how, when, and why to use FP concepts such as immutability and pure functions to write more concise, reasonable, and future-proof code.
"This practical book demonstrates why C++ is still one of the dominant production-quality languages for financial applications and systems. Many programmers believe that C++ is too difficult to learn. Author Daniel Hanson demonstrates that this is no longer the case, thanks to modern features added to the C++ Standard beginning in 2011. Financial programmers will discover how to leverage C++ abstractions that enable safe implementation of financial models. You'll also explore how popular open source libraries provide additional weapons for attacking mathematical problems. C++ programmers unfamiliar with financial applications also benefit from this handy guide."--
With technological advancements, fast markets, and higher complexity of systems, software engineers tend to skip the uncomfortable topic of software efficiency. However, tactical, observability-driven performance optimizations are vital for every product to save money and ensure business success.With this book, any engineer can learn how to approach software efficiency effectively, professionally, and without stress. Author Bartomiej Potka provides the tools and knowledge required to make your systems faster and less resource-hungry. Efficient Go guides you in achieving better day-to-day efficiency using Go. In addition, most content is language-agnostic, allowing you to bring small but effective habits to your programming or product management cycles.This book shows you how to: Clarify and negotiate efficiency goalsOptimize efficiency on various levelsUse common resources like CPU and memory effectivelyAssess efficiency using observability signals like metrics, logging, tracing, and (continuous) profiling via open source projects like Prometheus, Jaeger, and ParcaApply tools like go test, pprof, benchstat, and k6 to create reliable micro and macro benchmarksEfficiently use Go and its features like slices, generics, goroutines, allocation semantics, garbage collection, and more!
The cloud promises cost savings, agility, and more. But the increasing complexity of modern IT systems often prevents businesses from realizing the outcomes they sought by moving to the cloud in the first place. At the core of this complexity is technical debt. Ad hoc decisions, traditional approaches, and "e;quick and dirty"e; solutions add up to fragile, more complex systems (and organizations)--reducing the pace of change, driving up costs, and degrading security. To succeed in the cloud, you first have to manage the problems IT complexity creates. This practical guide will help.If you're tasked with designing, delivering, or approving IT systems that support business growth, this book will teach you the principles and strategies you need to break out of silos, reassess traditional thinking, provide the business a better competitive position, and achieve cloud success across the company.You'll learn:How to assess your current IT environment to identify complexity-centered risksHow to structure cross-functional teams to reduce friction pointsHow to create a framework for IT investments that's aligned with your business goalsHow to measure your outcomes for continued success
Many organizations today have begun to modernize their Windows workloads to take full advantage of cloud economics. If you're a C# developer at one of these companies, you need options for rehosting, replatforming, and refactoring your existing .NET Framework applications. This practical book guides you through the process of converting your monolithic application to microservices on AWS.Authors Noah Gift, founder of Pragmatic AI Labs, and James Charlesworth, engineering manager at Pendo, take you through the depth and breadth of .NET tools on AWS. You'll examine modernization techniques and pathways for incorporating Linux and Windows containers and serverless architecture to build, maintain, and scale modern .NET apps on AWS. With this book, you'll learn how to make your applications more modern, resilient, and cost-effective.Get started building solutions with C# on AWSLearn DevOps best practices for AWSExplore the development tools and services that AWS providesSuccessfully migrate a legacy .NET application to AWSDevelop serverless .NET microservices on AWSContainerize your .NET applications and move into the cloudMonitor and test your AWS .NET applicationsBuild cloud native solutions that combine the best of the .NET platform and AWS
Over the past 10 years, data analytics and data visualization have become essential components of an enterprise information strategy. And yet, the adoption of data analytics has remained remarkably static, reaching no more than 30% of potential users. This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations. Authors Donald Farmer and Jim Horbury show business users how to improve decision making without becoming analytics specialists. You'll explore different techniques for exchanging data, insights, and events between analytics platforms and hosting applications. You'll also examine issues including data governance and regulatory compliance and learn best practices for deploying and managing embedded analytics at scale. Learn how data analytics improves business decision making and performance Explore advantages and disadvantages of different embedded analytics platforms Develop a strategy for embedded analytics in an organization or product Define the architecture of an embedded solution Select vendors, platforms, and tools to implement your architecture Hire or train developers and architects to build the embedded solutions you need Understand how embedded analytics interacts with traditional analytics
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