- Table View
- List View
Practical Machine Learning in JavaScript: TensorFlow.js for Web Developers
by Charlie GerardBuild machine learning web applications without having to learn a new language. This book will help you develop basic knowledge of machine learning concepts and applications. You’ll learn not only theory, but also dive into code samples and example projects with TensorFlow.js. Using these skills and your knowledge as a web developer, you’ll add a whole new field of development to your tool set. This will give you a more concrete understanding of the possibilities offered by machine learning. Discover how ML will impact the future of not just programming in general, but web development specifically. Machine learning is currently one of the most exciting technology fields with the potential to impact industries from health to home automation to retail, and even art. Google has now introduced TensorFlow.js—an iteration of TensorFlow aimed directly at web developers. Practical Machine Learning in JavaScript will help you stay relevant in the tech industry with new tools, trends, and best practices.What You'll LearnUse the JavaScript framework for MLBuild machine learning applications for the webDevelop dynamic and intelligent web contentWho This Book Is ForWeb developers and who want a hands-on introduction to machine learning in JavaScript. A working knowledge of the JavaScript language is recommended.
Practical Machine Learning in R
by Mike Chapple Fred NwangangaGuides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.
Practical Machine Learning with AWS: Process, Build, Deploy, and Productionize Your Models Using AWS
by Himanshu SinghSuccessfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract.By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning—Specialty certification exam.What You Will LearnBe familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud FormationUnderstand SageMaker, Amazon Comprehend, and Amazon ForecastExecute live projects: from the pre-processing phase to deployment on AWSWho This Book Is ForMachine learning engineers who want to learn AWS machine learning services, and acquire an AWS machine learning specialty certification
Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI
by Darren CookMachine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning.Learn how to import, manipulate, and export data with H2OExplore key machine-learning concepts, such as cross-validation and validation data setsWork with three diverse data sets, including a regression, a multinomial classification, and a binomial classificationUse H2O to analyze each sample data set with four supervised machine-learning algorithmsUnderstand how cluster analysis and other unsupervised machine-learning algorithms work
Practical Machine Learning with R: Define, build, and evaluate machine learning models for real-world applications
by Monicah Wambugu Brindha Priyadarshini Jeyaraman Ludvig Renbo OlsenUnderstand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems Key Features Gain a comprehensive overview of different machine learning techniques Explore various methods for selecting a particular algorithm Implement a machine learning project from problem definition through to the final model Book Description With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you'll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you'll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it. What you will learn Define a problem that can be solved by training a machine learning model Obtain, verify and clean data before transforming it into the correct format for use Perform exploratory analysis and extract features from data Build models for neural net, linear and non-linear regression, classification, and clustering Evaluate the performance of a model with the right metrics Implement a classification problem using the neural net package Employ a decision tree using the random forest library Who this book is for If you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this book very useful. The book will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.
Practical Machine Learning with Rust: Creating Intelligent Applications in Rust
by Joydeep BhattacharjeeExplore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will LearnWrite machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.
Practical Machine Learning: A New Look at Anomaly Detection
by Ellen Friedman Ted DunningFinding Data Anomalies You Didn't Know to Look ForAnomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what "suspects" you're looking for. This O'Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work.From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle anomaly detection in your own project.Use probabilistic models to predict what's normal and contrast that to what you observeSet an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithmEstablish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic modelUse historical data to discover anomalies in sporadic event streams, such as web trafficLearn how to use deviations in expected behavior to trigger fraud alerts
Practical Machine Learning: A New Look at Anomaly Detection
by Ellen Friedman Ted DunningFinding Data Anomalies You Didn't Know to Look ForAnomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what “suspects” you’re looking for. This O’Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work.From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle anomaly detection in your own project.Use probabilistic models to predict what’s normal and contrast that to what you observeSet an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithmEstablish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic modelUse historical data to discover anomalies in sporadic event streams, such as web trafficLearn how to use deviations in expected behavior to trigger fraud alerts
Practical Machine Learning: Innovations in Recommendation
by Ellen Friedman Ted DunningBuilding a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings—and demonstrates how even a small-scale development team can design an effective large-scale recommendation system.Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You’ll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time.Understand the tradeoffs between simple and complex recommendersCollect user data that tracks user actions—rather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques
Practical Maintenance Plans in SQL Server
by Bradley BeardThis book is a complete guide to setting up and maintaining maintenance plans for SQL Server Database Administrators. Maintenance plans too often consist of a backup task and that's it, but there is so much more that can and must be done to ensure the integrity of your most important company resource - the data you are tasked to manage and safeguard. This book walks even the newest of users through creating a powerful, automated maintenance plan. Automate your job using SQL Server Agent to leverage the power of Maintenance Plans to deliver real, proactive solutions to common issues. Schedule common tasks such as backups and index rebuilds to run automatically, and get early-warning notifications of impending problems relating to resource usage and query performance. By the time your boss knows to call you about a problem, you'll have already called him to describe your solution. The large majority of books never really cover the topic of inheriting a database server with multiple live databases; the common thread is that the databases will be created and maintained by the reader forever and ever. In the real world, that scenario rarely happens. Practical Maintenance Plans in SQL Server covers that scenario and provides you with the knowledge and tools needed to get comfortable writing your own maintenance plans for any SQL Server database, whether created by you or inherited. Shows the different tasks that can be run in a maintenance plan. Explains how and why those tasks can be implemented. Provides a roadmap to creating your own custom maintenance plan. What you'll learn Implement a completely automated backup maintenance plan Be alerted to performance problems and outages ahead of your boss Learn the different types of database maintenance tasks Plan the workflow of tasks within a maintenance plan Automate your work by implementing custom maintenance plans Who this book is for Practical Maintenance Plans in SQL Server is for any level of database administrator, but specifically it's for those administrators with a real need to set up a powerful maintenance plan quickly. New and seasoned administrators will appreciate the book for its robust learning pattern of visual aids in combination with explanations and scenarios. Practical Maintenance Plans in SQL Server is the perfect "new hire" gift for new database administrators in any organization.
Practical Malware Analysis: A Hands-On Guide to Dissecting Malicious Software
by Michael Sikorski Andrew HonigMalware analysis is big business, and attacks can cost a company dearly. When malware breaches your defenses, you need to act quickly to cure current infections and prevent future ones from occurring.For those who want to stay ahead of the latest malware, Practical Malware Analysis will teach you the tools and techniques used by professional analysts. With this book as your guide, you'll be able to safely analyze, debug, and disassemble any malicious software that comes your way.You'll learn how to:–Set up a safe virtual environment to analyze malware–Quickly extract network signatures and host-based indicators–Use key analysis tools like IDA Pro, OllyDbg, and WinDbg–Overcome malware tricks like obfuscation, anti-disassembly, anti-debugging, and anti-virtual machine techniques–Use your newfound knowledge of Windows internals for malware analysis–Develop a methodology for unpacking malware and get practical experience with five of the most popular packers–Analyze special cases of malware with shellcode, C++, and 64-bit codeHands-on labs throughout the book challenge you to practice and synthesize your skills as you dissect real malware samples, and pages of detailed dissections offer an over-the-shoulder look at how the pros do it. You'll learn how to crack open malware to see how it really works, determine what damage it has done, thoroughly clean your network, and ensure that the malware never comes back.Malware analysis is a cat-and-mouse game with rules that are constantly changing, so make sure you have the fundamentals. Whether you're tasked with securing one network or a thousand networks, or you're making a living as a malware analyst, you'll find what you need to succeed in Practical Malware Analysis.
Practical Malware Analysis: The Hands-on Guide to Dissecting Malicious Software
by Michael Sikorski Andrew HonigMalware analysis is big business, and attacks can cost a company dearly. When malware breaches your defenses, you need to act quickly to cure current infections and prevent future ones from occurring. For those who want to stay ahead of the latest malware, Practical Malware Analysis will teach you the tools and techniques used by professional analysts. With this book as your guide, you'll be able to safely analyze, debug, and disassemble any malicious software that comes your way. You'll learn how to: Set up a safe virtual environment to analyze malware Quickly extract network signatures and host-based indicators Use key analysis tools like IDA Pro, OllyDbg, and WinDbg Overcome malware tricks like obfuscation, anti-disassembly, anti-debugging, and anti-virtual machine techniques Use your newfound knowledge of Windows internals for malware analysis Develop a methodology for unpacking malware and get practical experience with five of the most popular packers Analyze special cases of malware with shellcode, C++, and 64-bit code Hands-on labs throughout the book challenge you to practice and synthesize your skills as you dissect real malware samples, and pages of detailed dissections offer an over-the-shoulder look at how the pros do it. You'll learn how to crack open malware to see how it really works, determine what damage it has done, thoroughly clean your network, and ensure that the malware never comes back. Malware analysis is a cat-and-mouse game with rules that are constantly changing, so make sure you have the fundamentals. Whether you're tasked with securing one network or a thousand networks, or you're making a living as a malware analyst, you'll find what you need to succeed in Practical Malware Analysis.
Practical Mathematical Cryptography (Chapman & Hall/CRC Cryptography and Network Security Series)
by Kristian GjøsteenPractical Mathematical Cryptography provides a clear and accessible introduction to practical mathematical cryptography. Cryptography, both as a science and as practice, lies at the intersection of mathematics and the science of computation, and the presentation emphasises the essential mathematical nature of the computations and arguments involved in cryptography. Cryptography is also a practical science, and the book shows how modern cryptography solves important practical problems in the real world, developing the theory and practice of cryptography from the basics to secure messaging and voting. The presentation provides a unified and consistent treatment of the most important cryptographic topics, from the initial design and analysis of basic cryptographic schemes towards applications. Features Builds from theory toward practical applications Suitable as the main text for a mathematical cryptography course Focus on secure messaging and voting systems.
Practical Maya Programming with Python
by Robert Galanakis"Practical Maya Programming with Python" is a practical tutorial packed with plenty of examples and sample projects which guides you through building reusable, independent modules and handling unexpected errors. If you are a developer looking to build a powerful system using Python and Maya's capabilities, then this book is for you. Practical Maya Programming with Python is perfect for intermediate users with basic experience in Python and Maya who want to better their knowledge and skills.
Practical Memory Forensics: Jumpstart effective forensic analysis of volatile memory
by Oleg Skulkin Svetlana OstrovskayaA practical guide to enhancing your digital investigations with cutting-edge memory forensics techniquesKey FeaturesExplore memory forensics, one of the vital branches of digital investigationLearn the art of user activities reconstruction and malware detection using volatile memoryGet acquainted with a range of open-source tools and techniques for memory forensicsBook DescriptionMemory Forensics is a powerful analysis technique that can be used in different areas, from incident response to malware analysis. With memory forensics, you can not only gain key insights into the user's context but also look for unique traces of malware, in some cases, to piece together the puzzle of a sophisticated targeted attack.Starting with an introduction to memory forensics, this book will gradually take you through more modern concepts of hunting and investigating advanced malware using free tools and memory analysis frameworks. This book takes a practical approach and uses memory images from real incidents to help you gain a better understanding of the subject and develop the skills required to investigate and respond to malware-related incidents and complex targeted attacks. You'll cover Windows, Linux, and macOS internals and explore techniques and tools to detect, investigate, and hunt threats using memory forensics. Equipped with this knowledge, you'll be able to create and analyze memory dumps on your own, examine user activity, detect traces of fileless and memory-based malware, and reconstruct the actions taken by threat actors.By the end of this book, you'll be well-versed in memory forensics and have gained hands-on experience of using various tools associated with it.What you will learnUnderstand the fundamental concepts of memory organizationDiscover how to perform a forensic investigation of random access memoryCreate full memory dumps as well as dumps of individual processes in Windows, Linux, and macOSAnalyze hibernation files, swap files, and crash dumpsApply various methods to analyze user activitiesUse multiple approaches to search for traces of malicious activityReconstruct threat actor tactics and techniques using random access memory analysisWho this book is forThis book is for incident responders, digital forensic specialists, cybersecurity analysts, system administrators, malware analysts, students, and curious security professionals new to this field and interested in learning memory forensics. A basic understanding of malware and its working is expected. Although not mandatory, knowledge of operating systems internals will be helpful. For those new to this field, the book covers all the necessary concepts.
Practical Methods for Legal Investigations: Concepts and Protocols in Civil and Criminal Cases
by CLI, Dean BeersLegal investigators are responsible for providing factual evidence � as the fact finders, they are the foundation for the attorneys they work with daily. The attorney is responsible for forming and implementing the legal strategy and presenting it to the judge or jury. The legal investigator provides checks and balances to ensure that no evidence i
Practical Microcontroller Engineering with ARM Technology
by Ying BaiThis book covers both the fundamentals, as well as practical techniques in designing and building microcontrollers in industrial and commercial applications. Examples included in this book have been compiled, built, and tested Includes Both ARM® assembly and C codes Direct Register Access (DRA) model and the Software Driver (SD) model programming techniques and discussed If you are an instructor and adopted this book for your course, please email ieeeproposals@wiley. com to get access to the instructor files for this book.
Practical Microservices
by Umesh Ram SharmaLearn how to implement the microservice architecture using Java About This Book • Leverage the power of microservices to build a flexible and efficient system in Java • See Docker and Spring Boot in practice to form easily deployable microservices • Hands-on approach throughout the book in order to familiarize and grasp the details Who This Book Is For This book is for Java developers who want to get started with microservices and implement it in their workplace. No knowledge of microservice is necessary. What You Will Learn • The role of a discovery service and externalized configuration in the overall architecture • Use of message brokers for event driven microservices • How to intermix data management strategies across components • Implementing different types of tests in Spring Boot environment • Applying CI to our microservices style architecture • Walk through of monitoring and scaling the sample application In Detail A microservice architecture helps you build your application as a suite of different services. This approach has been widely adopted as it helps to easily scale up your application with reduced dependencies. This way if a part of your application is corrupted, it can be fixed easily thereby eliminating the possibility of completely shutting down your software. This book will teach you how to leverage Java to build scalable microservices. You will learn the fundamentals of this architecture and how to efficiently implement it practically. We start off with a brief introduction to the microservice architecture and how it fares with the other architectures. The book dives deep into essential microservice components and how to set up seamless communication between two microservice end points. You will create an effective data model and learn different ways to test and deploy a microservices. You will also learn the best way to migrate your software from a monolith to a microservice architecture. Finishing off with monitoring, scaling and troubleshooting, this book will set a solid foundation for you to start implementing microservices. Style and approach Starting with the fundamentals, this book explains all the essential concepts gradually with the help of numerous examples.
Practical Microservices Architectural Patterns: Event-Based Java Microservices with Spring Boot and Spring Cloud
by Binildas ChristudasTake your distributed applications to the next level and see what the reference architectures associated with microservices can do for you. This book begins by showing you the distributed computing architecture landscape and provides an in-depth view of microservices architecture. Following this, you will work with CQRS, an essential pattern for microservices, and get a view of how distributed messaging works. Moving on, you will take a deep dive into Spring Boot and Spring Cloud. Coming back to CQRS, you will learn how event-driven microservices work with this pattern, using the Axon 2 framework. This takes you on to how transactions work with microservices followed by advanced architectures to address non-functional aspects such as high availability and scalability. In the concluding part of the book you develop your own enterprise-grade microservices application using the Axon framework and true BASE transactions, while making it as secure as possible.What You Will LearnShift from monolith architecture to microservices Work with distributed and ACID transactionsBuild solid architectures without two-phase commit transactionsDiscover the high availability principles in microservicesWho This Book Is ForJava developers with basic knowledge of distributed and multi-threaded application architecture, and no knowledge of Spring Boot or Spring Cloud. Knowledge of CQRS and event-driven architecture is not mandatory as this book will cover these in depth.
Practical Microservices with Dapr and .NET: A developer's guide to building cloud-native applications using the Dapr event-driven runtime
by Mark Russinovich Davide BedinUse the new, enticing, and highly portable event-driven runtime to simplify building resilient and scalable microservices for cloud and edge applicationsKey FeaturesBuild resilient, stateless, and stateful microservice applications that run on the cloud and edgeSolve common distributed systems such as low latency and scaling using any language and frameworkUse real-time and proactive monitoring tools to support a reliable and highly available systemBook DescriptionOver the last decade, there has been a huge shift from heavily coded monolithic applications to finer, self-contained microservices. Dapr is a new, open source project by Microsoft that provides proven techniques and best practices for developing modern applications. It offers platform-agnostic features for running your applications on public cloud, on-premises, and even on edge devices. This book will help you get to grips with microservice architectures and how to manage application complexities with Dapr in no time. You'll understand how Dapr offers ease of implementation while allowing you to work with multiple languages and platforms. You'll also understand how Dapr's runtime, services, building blocks, and software development kits (SDKs) help you to simplify the creation of resilient and portable microservices. Dapr provides an event-driven runtime that supports the essential features you need to build microservices, including service invocation, state management, and publish/subscribe messaging. You'll explore all of those in addition to various other advanced features with this practical guide to learning Dapr. By the end of this book, you'll be able to write microservices easily using your choice of language or framework by implementing industry best practices to solve problems related to distributed systems.What you will learnUse Dapr to create services, invoking them directly and via pub/subDiscover best practices for working with microservice architecturesLeverage the actor model to orchestrate data and behaviorUse Azure Kubernetes Service to deploy a sample applicationMonitor Dapr applications using Zipkin, Prometheus, and GrafanaScale and load test Dapr applications on KubernetesWho this book is forThis book is for developers looking to explore microservices architectures and implement them in Dapr applications using examples on Microsoft .NET Core. Whether you are new to microservices or have knowledge of this architectural approach and want to get hands-on experience in using Dapr, you'll find this book useful. Familiarity with .NET Core will help you to understand the C# samples and code snippets used in the book.
Practical Microservices with Dapr and .NET: A developer's guide to building cloud-native applications using the event-driven runtime, 2nd Edition
by Mark Russinovich Davide BedinUse the innovative, highly portable event-driven distributed application runtime to simplify building resilient and scalable microservices for cloud and edge applications.Purchase of the print or Kindle book includes a free eBook in the PDF format.Key FeaturesBuild resilient, stateless, and stateful microservice applications that run on the cloud and edgeOvercome common issues in distributed systems, such as low latency and scaling, using any language and frameworkLearn how to expose and operate Dapr applications with multiple optionsBook DescriptionThis second edition will help you get to grips with microservice architectures and how to manage application complexities with Dapr in no time. You'll understand how Dapr simplifies development while allowing you to work with multiple languages and platforms. Following a C# sample, you'll understand how Dapr's runtime, building blocks, and software development kits (SDKs) help you to simplify the creation of resilient and portable microservices.Dapr provides an event-driven runtime that supports the essential features you need for building microservices, including service invocation, state management, and publish/subscribe messaging. You'll explore all of those in addition to various other advanced features with this practical guide to learning Dapr. With a focus on deploying the Dapr sample application to an Azure Kubernetes Service cluster and to the Azure Container Apps serverless platform, you'll see how to expose the Dapr application with NGINX, YARP, and Azure API Management.By the end of this book, you'll be able to write microservices easily by implementing industry best practices to solve problems related to distributed systems.What you will learnUse Dapr to create services, invoking them directly and via pub/subDiscover best practices for working with microservice architecturesLeverage the actor model to orchestrate data and behaviorExpose API built with Dapr applications via NGINX and Azure API ManagementUse Azure Kubernetes Service to deploy a sample applicationMonitor Dapr applications using Zipkin, Prometheus, and GrafanaScale and load test Dapr applications on KubernetesGet to grips with Azure Container Apps as you combine Dapr with a serverless platformWho this book is forThis book is for developers looking to explore and implement microservices architectures in Dapr applications using .NET examples. Whether you are new to microservices or have knowledge of this architectural approach and want to get hands-on experience using Dapr, you'll find this book useful. Familiarity with .NET will help you to understand the C# samples and code snippets used in the book.
Practical Microservices: Build Event-Driven Architectures with Event Sourcing and CQRS
by Ethan GarofoloMVC and CRUD make software easier to write, but harder to change. Microservice-based architectures can help even the smallest of projects remain agile in the long term, but most tutorials meander in theory or completely miss the point of what it means to be microservice-based. Roll up your sleeves with real projects and learn the most important concepts of evented architectures. You'll have your own deployable, testable project and a direction for where to go next. Much ink has been spilled on the topic of microservices, but all of this writing fails to accurately identity what makes a system a monolith, define what microservices are, or give complete, practical examples, so you're probably left thinking they have nothing to offer you. You don't have to be at Google or Facebook scale to benefit from a microservice-based architecture. Microservices will keep even small and medium teams productive by keeping the pieces of your system focused and decoupled. Discover the basics of message-based architectures, render the same state in different shapes to fit the task at hand, and learn what it is that makes something a monolith (it has nothing to do with how many machines you deploy to). Conserve resources by performing background jobs with microservices. Deploy specialized microservices for registration, authentication, payment processing, e-mail, and more. Tune your services by defining appropriate service boundaries. Deploy your services effectively for continuous integration. Master debugging techniques that work across different services. You'll finish with a deployable system and skills you can apply to your current project.Add the responsiveness and flexibility of microservices to your project, no matter what the size or complexity.What You Need:While the principles of this book transcend programming language, the code examples are in Node.js because JavaScript, for better or worse, is widely read. You'll use PostgreSQL for data storage, so familiarity with it is a plus. The books does provide Docker images to make working with PostgreSQL a bit easier, but extensive Docker knowledge is not required.
Practical Microsoft Azure IaaS: Migrating And Building Scalable And Secure Cloud Solutions
by Shijimol Ambi KarthikeyanAdopt Azure IaaS and migrate your on-premise infrastructure partially or fully to Azure. This book provides practical solutions by following Microsoft’s design and best practice guidelines for building highly available, scalable, and secure solution stacks using Microsoft Azure IaaS. The author starts by giving an overview of Azure IaaS and its components: you’ll see the new aspects of Azure Resource Manager, storage in IaaS, and Azure networking. As such, you’ll cover design considerations for migration and implementation of infrastructure services, giving you practical skills to apply to your own projects. The next part of the book takes you through the different components of Azure IaaS that need to be included in a resilient architecture and how to set up a highly available infrastructure in Azure. The author focuses on the tools available for Azure IaaS automated provisioning and the different performance monitoring and fine-tuning options available for the platform. Finally, you’ll gain practical skills in Azure security and implementing Azure architectures.After reading Practical Microsoft Azure IaaS, you will have learned how to map the familiar on-premise architecture components to their cloud infrastructure counterparts. This book provides a focused and practical approach to designing solutions to be hosted in Azure IaaS.What You Will LearnMap the key Azure components to familiar concepts in infrastructure, such as virtualization, storage provisioning, switching, and firewallsImplement Azure IaaS deployment architectures Design IaaS environments in line with the Microsoft recommended best practices for scalability, resiliency, availability, performance, and securityManage the operational aspects of hosted environments, leverage automation, and fine tune for optimal performanceWho This Book Is ForInfrastructure and solution architects with skills in on-premise infrastructure design who want to up-skill in Azure IaaS.
Practical Microsoft Visual Studio 2015
by Peter RitchieLearn the details of the most highly recommended practices of software development using the latest version of Visual Studio 2015. Recommended practices are grouped by development phase and explained in far more detail than the typical tips and tricks compilations. This book also contains detailed coverage of recognized patterns and practices used to create software in a timely manner with expected quality in the context of using specific Visual Studio 2015 features. Creating software is part defined process and part empirical process. While there is no single "best" process to employ in all development scenarios, MVP author Peter Ritchie helps readers navigate the complexity of development options and decide which techniques and Visual Studio 2015 features to use based on the needs of their particular project. Readers will learn practices such as those related to working in teams, design and architecture, refactoring, source code control workflows, unit testing, performance testing, coding practices, use of common patterns, code analysis, IDE extensions, and more. What You Will Learn Use patterns and practices within Visual Studio Implement practices of software creation Work in teams Develop workflows for software projects Who This Book Is For Beginning and intermediate software developers and architects
Practical Mobile Forensics
by Rohit Tamma Heather Mahalik Satish BommisettyThe book is an easy-to-follow guide with clear instructions on various mobile forensic techniques. The chapters and the topics within are structured for a smooth learning curve, which will swiftly empower you to master mobile forensics. If you are a budding forensic analyst, consultant, engineer, or a forensic professional wanting to expand your skillset, this is the book for you. The book will also be beneficial to those with an interest in mobile forensics or wanting to find data lost on mobile devices. It will be helpful to be familiar with forensics in general but no prior experience is required to follow this book.