Machine Learning Engineering on AWS
Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycleKey Features: Gain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and moreUse container and serverless services to solve a variety of ML engineering requirementsDesign, build, and secure automated MLOps pipelines and workflows on AWSBook Description: There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production.This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS.By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements.What You Will Learn: Find out how to train and deploy TensorFlow and PyTorch models on AWSUse containers and serverless services for ML engineering requirementsDiscover how to set up a serverless data warehouse and data lake on AWSBuild automated end-to-end MLOps pipelines using a variety of servicesUse AWS Glue DataBrew and SageMaker Data Wrangler for data engineeringExplore different solutions for deploying deep learning models on AWSApply cost optimization techniques to ML environments and systemsPreserve data privacy and model privacy using a variety of techniquesWho this book is for: This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
Explainable Human-AI Interaction
From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans--swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), andbe ready to provide customized explanations when needed. The authors draw from several years of research in their lab to discuss how an AI agent can use these mental models to either conform to human expectations or change those expectations through explanatory communication. While the focus of the book is on cooperative scenarios, it also covers how the same mental models can be used for obfuscation and deception. The book also describes several real-world application systems for collaborative decision-making that are based on the framework and techniques developed here. Although primarily driven by the authors' own research in these areas, every chapter will provide ample connections to relevant research from the wider literature. The technical topics covered in the book are self-contained and are accessible to readers with a basic background in AI.
Data Storytelling with Google Data Studio
Apply data storytelling concepts and analytical thinking to create dashboards and reports in Google Data Studio to aid data-driven decision makingKey Features: Gain a solid understanding of data visualization principles and learn to apply them effectivelyGet to grips with the concepts and features of Data Studio to create powerful data storiesExplore the end-to-end process of building dashboards with the help of practical examplesBook Description: Presenting data visually makes it easier for organizations and individuals to interpret and analyze information. Google Data Studio is an easy-to-use, collaborative tool that enables you to transform your data into engaging visualizations. This allows you to build and share dashboards that help monitor key performance indicators, identify patterns, and generate insights to ultimately drive decisions and actions.Data Storytelling with Google Data Studio begins by laying out the foundational design principles and guidelines that are essential to creating accurate, effective, and compelling data visualizations. Next, you'll delve into features and capabilities of Data Studio - from basic to advanced - and explore their application with examples. The subsequent chapters walk you through building dashboards with a structured three-stage process called the 3D approach using real-world examples that'll help you understand the various design and implementation considerations. This approach involves determining the objectives and needs of the dashboard, designing its key components and layout, and developing each element of the dashboard.By the end of this book, you will have a solid understanding of the storytelling approach and be able to create data stories of your own using Data Studio.What You Will Learn: Understand what storytelling with data means, and explore its various formsDiscover the 3D approach to building dashboards - determine, design, and developTest common data visualization pitfalls and learn how to mitigate themGet up and running with Data Studio and leverage it to explore and visualize dataExplore the advanced features of Data Studio with examplesBecome well-versed in the step-by-step process of the 3D approach using practical examplesMeasure and monitor the usage patterns of your Data Studio reportsWho this book is for: If you are a beginner or an aspiring data analyst looking to understand the core concepts of data visualization and want to use Google Data Studio for creating effective dashboards, this book is for you. No specific prior knowledge is needed to understand the concepts present in this book. Experienced data analysts and business intelligence developers will also find this book useful as a detailed guide to using Data Studio as well as a refresher of core dashboarding concepts.
Scalable Data Architecture with Java
Orchestrate data architecting solutions using Java and related technologies to evaluate, recommend and present the most suitable solution to leadership and clientsKey Features: Learn how to adapt to the ever-evolving data architecture technology landscapeUnderstand how to choose the best suited technology, platform, and architecture to realize effective business valueImplement effective data security and governance principlesBook Description: Java architectural patterns and tools help architects to build reliable, scalable, and secure data engineering solutions that collect, manipulate, and publish data.This book will help you make the most of the architecting data solutions available with clear and actionable advice from an expert.You'll start with an overview of data architecture, exploring responsibilities of a Java data architect, and learning about various data formats, data storage, databases, and data application platforms as well as how to choose them. Next, you'll understand how to architect a batch and real-time data processing pipeline. You'll also get to grips with the various Java data processing patterns, before progressing to data security and governance. The later chapters will show you how to publish Data as a Service and how you can architect it. Finally, you'll focus on how to evaluate and recommend an architecture by developing performance benchmarks, estimations, and various decision metrics.By the end of this book, you'll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients.What You Will Learn: Analyze and use the best data architecture patterns for problemsUnderstand when and how to choose Java tools for a data architectureBuild batch and real-time data engineering solutions using JavaDiscover how to apply security and governance to a solutionMeasure performance, publish benchmarks, and optimize solutionsEvaluate, choose, and present the best architectural alternativesUnderstand how to publish Data as a Service using GraphQL and a REST APIWho this book is for: Data architects, aspiring data architects, Java developers and anyone who wants to develop or optimize scalable data architecture solutions using Java will find this book useful. A basic understanding of data architecture and Java programming is required to get the best from this book.
SX001G, Glossary for the S-Series IPS specifications, Issue 3.0
The Glossary for the S-Series IPS specifications is a compilation of the description of all classes and attributes shared by the full set of the S-Series of Integrated Product Support (IPS) specifications. It also includes common terms used by the S-Series.
Unity 3D Game Development
Create ready-to-play 3D games with reactive environments, sound, dynamic effects, and more!Key Features: Build a solid foundation for game design and game developmentUnderstand the fundamentals of 3D such as coordinates, spaces, vectors, and camerasGet to grips with essential Unity concepts including characters, scenes, terrains, objects and moreBook Description: This book, written by a team of experts at Unity Technologies, follows an informal, demystifying approach to the world of game development.Within Unity 3D Game Development, you will learn to: Design and build 3D characters, and the game environmentThink about the users' interactions with your gameDevelop the interface and apply visual effects to add an emotional connection to your worldGrasp a solid foundation of sound design, animations, and lightning to your creationsBuild, test, and add final touchesThe book is split between expert insights that you'll read before you look into the project on GitHub to understand all the underpinnings. This way, you get to see the end result, and you're allowed to be creative and give your own thoughts to design, as well as work through the process with the new tools we introduce.Join the book community on Discord: Read this book with Unity game developers, and the team of authors. Ask questions, build teams, chat with the authors, participate in events and much more. The link to join is included in the book.What You Will Learn: Learn fundamentals of designing a 3D game and C# scriptingDesign your game character and work through their mechanics and movementsCreate an environment with Unity Terrain and ProBuilderExplore instantiation and rigid bodies through physics theory and codeImplement sound, lighting effects, trail rendering, and other dynamic effectsCreate a short, fully functional segment of your game in a vertical slicePolish your game with performance tweaksJOIN the 'book-club' to read alongside other users, Unity experts, and ask the authors when stuckWho this book is for: Our goal with this book is to enable every reader to build the right mindset to think about 3D games, and then show them all the steps we took to create ours.The main target audience for this book is those with some prior knowledge in game development, though regardless of your experience, we hope to create an enjoyable learning journey for you
Towards Autonomous Robotic Systems
The volume LNAI 13546 constitutes the refereed proceedings of the 23rd Annual Conference Towards Autonomous Robotic Systems, TAROS 2022, held in Culham, UK, in September 2022. The 14 full papers and 10 short papers were carefully reviewed and selected from 38 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: Robotic Grippers and Manipulation; Soft Robotics, Sensing and Mobile Robots; Robotic Learning, Mapping and Planning; Robotic Systems and Applications.
Self Aware Security for Real Time Task Schedules in Reconfigurable Hardware Platforms
Chapter 1: Introduction(a) Reconfigurable hardware based embedded systems (b) Importance of Real Time Scheduling for such embedded architectures (c) Importance of Self Aware Security for such architectures Chapter 2: Background (a) Scheduling for embedded real time tasks and limitations of existing techniques (b) Security related to hardware attacks and limitations of existing techniques Chapter 3: A novel real-time scheduling for FPGAs having slotted area model This chapter presents deadline-partition oriented scheduling methodologiesfor periodic hard real-time dynamic task sets on fully and partiallyreconfigurable FPGAs in which the floor of the FPGA is assumed to be statically equi-partitioned into a set of homogeneous tiles such that anyarbitrary task of the given task set may be feasibly mapped into the areaof a given tile. Chapter 4: A novel real-time scheduling for FPGAs having flexible area model This chapter presents scheduling methodologies for periodic dependent hard real-time dynamic task sets on fully and partially reconfigurable FPGAs in which the floor of the FPGA follows flexible area model such that any task can be placed anywhere within the floor area. This will work will attempt to solve both the temporal and spatial aspects of the scheduling. Chapter 5: Denial of Service Attacks for Real Time Scheduling and Related Mitigation Techniques This chapter presents threat analysis associated with denial of service attacks due to delay inducing hardware trojans in embedded architectures for the scheduling strategies presenteed in Chapter 3 and 4. A self aware security module is also presented that detects and mitigates the threat. Chapter 6: Erroneous Result Generation Attack for Real Time Scheduling and Related Mitigation Technique This chapter presents threat analysis associated with generation of erroneous results that may jeopardize the real time task schedules presented in Chapter 3 and 4. Related detection and mitigation techniques are presented alongwith. In addition to this, it is also described how related modifications of the self aware security module can ensure security for the present scenario.Chapter 7: Conclusion In this book, we present the importance of real time scheduling for reconfigurable hardware based embedded platforms and related security needs. We present limitations of existing techniques and present some new real time scheduling techniques suitable for the embedded platform. We also focus on how denial of service and erroneous result generation may take place on the real time schedules due to vulnerability of hardware. Related detection and mitigation techniques are discussed, along with description of a self aware module that facilitates detection and mitigation from such threats.
The Nature of Data
When we look at some of the most pressing issues in environmental politics today, it is hard to avoid data technologies. Big data, artificial intelligence, and data dashboards all promise "revolutionary" advances in the speed and scale at which governments, corporations, conservationists, and even individuals can respond to environmental challenges. By bringing together scholars from geography, anthropology, science and technology studies, and ecology, The Nature of Data explores how the digital realm is a significant site in which environmental politics are waged. This collection as a whole makes the argument that we cannot fully understand the current conjuncture in critical, global environmental politics without understanding the role of data platforms, devices, standards, and institutions. In particular, The Nature of Data addresses the contested practices of making and maintaining data infrastructure, the imaginaries produced by data infrastructures, the relations between state and civil society that data infrastructure reworks, and the conditions under which technology can further socio-ecological justice instead of re-entrenching state and capitalist power. This innovative volume presents some of the first research in this new but rapidly growing subfield that addresses the role of data infrastructures in critical environmental politics.
The Nature of Data
When we look at some of the most pressing issues in environmental politics today, it is hard to avoid data technologies. Big data, artificial intelligence, and data dashboards all promise "revolutionary" advances in the speed and scale at which governments, corporations, conservationists, and even individuals can respond to environmental challenges. By bringing together scholars from geography, anthropology, science and technology studies, and ecology, The Nature of Data explores how the digital realm is a significant site in which environmental politics are waged. This collection as a whole makes the argument that we cannot fully understand the current conjuncture in critical, global environmental politics without understanding the role of data platforms, devices, standards, and institutions. In particular, The Nature of Data addresses the contested practices of making and maintaining data infrastructure, the imaginaries produced by data infrastructures, the relations between state and civil society that data infrastructure reworks, and the conditions under which technology can further socio-ecological justice instead of re-entrenching state and capitalist power. This innovative volume presents some of the first research in this new but rapidly growing subfield that addresses the role of data infrastructures in critical environmental politics.
Artificial Intelligence and Industry 4.0
Artificial Intelligence and Industry 4.0 explores recent advancements in blockchain technology and artificial intelligence (AI) as well as their crucial impacts on realizing Industry 4.0 goals. The book explores AI applications in industry including Internet of Things (IoT) and Industrial Internet of Things (IIoT) technology. Chapters explore how AI (machine learning, smart cities, healthcare, Society 5.0, etc.) have numerous potential applications in the Industry 4.0 era. This book is a useful resource for researchers and graduate students in computer science researching and developing AI and the IIoT.
Intelligent Robotics and Applications
The 4-volume set LNAI 13455 - 13458 constitutes the proceedings of the 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022, which took place in Harbin China, during August 2022.The 284 papers included in these proceedings were carefully reviewed and selected from 442 submissions. They were organized in topical sections as follows: Robotics, Mechatronics, Applications, Robotic Machining, Medical Engineering, Soft and Hybrid Robots, Human-robot Collaboration, Machine Intelligence, and Human Robot Interaction.
Intelligent Robotics and Applications
The 4-volume set LNAI 13455 - 13458 constitutes the proceedings of the 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022, which took place in Harbin China, during August 2022.The 284 papers included in these proceedings were carefully reviewed and selected from 442 submissions. They were organized in topical sections as follows: Robotics, Mechatronics, Applications, Robotic Machining, Medical Engineering, Soft and Hybrid Robots, Human-robot Collaboration, Machine Intelligence, and Human Robot Interaction.
Intelligent Robotics and Applications
The 4-volume set LNAI 13455 - 13458 constitutes the proceedings of the 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022, which took place in Harbin China, during August 2022.The 284 papers included in these proceedings were carefully reviewed and selected from 442 submissions. They were organized in topical sections as follows: Robotics, Mechatronics, Applications, Robotic Machining, Medical Engineering, Soft and Hybrid Robots, Human-robot Collaboration, Machine Intelligence, and Human Robot Interaction.
Microsoft Azure Networking
Proven best practices for success with every Azure networking service For cloud environments to operate and scale optimally, their networking services must be designed, deployed, and managed well. Now, there's a complete, best-practice guide to doing just that. Writing for everyone involved in delivering Azure workloads and services, leading cloud consultant Avinash Valiramani provides a deep dive and practical field advice for Azure Virtual Networks, Azure VPN Gateways, Azure Load Balancing, Azure Traffic Manager, Azure Firewall, Azure DNS, Azure Bastion, Azure Front Door and more. Whatever your role in delivering efficient, scalable networking services, this guide will help you make the most of your Azure investment. Leading Azure consultant Avinash Valiramani shows how to: Use Azure Virtual Networks to establish a backbone for hosting other Azure resources Provide HTTP/HTTPS load-balancing and routing for web servers and apps through Azure Application Gateway Connect on-premises and other public networks to Azure for secure communications using the Azure VPN Gateway service Provide secure load balancing to apps from internal and public networks using Azure Load Balancer services Integrate Azure Firewall to centrally protect Azure resources across multiple subscriptions Access globally scaled, fully-managed DNS services with 100% SLA from the closest Azure DNS servers Provide optimal network routing to the closest application endpoint for public-facing applications with Azure Traffic Manager Use Microsoft's global edge network along with Azure Front Door to speed up access, harden security and enhance scalability for consuming-facing and internal web applications Also look for these Definitive Guides to Azure success: Microsoft Azure Compute: The Definitive Guide Microsoft Azure Monitoring and Management: The Definitive Guide Microsoft Azure Storage: The Definitive Guide
Intelligent Robotics and Applications
The 4-volume set LNAI 13455 - 13458 constitutes the proceedings of the 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022, which took place in Harbin China, during August 2022.The 284 papers included in these proceedings were carefully reviewed and selected from 442 submissions. They were organized in topical sections as follows: Robotics, Mechatronics, Applications, Robotic Machining, Medical Engineering, Soft and Hybrid Robots, Human-robot Collaboration, Machine Intelligence, and Human Robot Interaction.
Data Cleaning and Exploration with Machine Learning
Explore supercharged machine learning techniques to take care of your data laundry loadsKey Features: Learn how to prepare data for machine learning processesUnderstand which algorithms are based on prediction objectives and the properties of the dataExplore how to interpret and evaluate the results from machine learningBook Description: Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results.As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You'll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you'll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You'll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book.By the end of this book, you'll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.What You Will Learn: Explore essential data cleaning and exploration techniques to be used before running the most popular machine learning algorithmsUnderstand how to perform preprocessing and feature selection, and how to set up the data for testing and validationModel continuous targets with supervised learning algorithmsModel binary and multiclass targets with supervised learning algorithmsExecute clustering and dimension reduction with unsupervised learning algorithmsUnderstand how to use regression trees to model a continuous targetWho this book is for: This book is for professional data scientists, particularly those in the first few years of their career, or more experienced analysts who are relatively new to machine learning. Readers should have prior knowledge of concepts in statistics typically taught in an undergraduate introductory course as well as beginner-level experience in manipulating data programmatically.
Database Design Using Entity-Relationship Diagrams
Thoroughly revised and updated, this third edition covers, in an intuitive, way, the database design process, from the inception of a database to effectively mapping the design to a relational model, which can then be implemented in any relational software. Students also learn how to reverse engineer a database from relational mappings.
Agents and Artificial Intelligence
This book constitutes selected papers from the refereed proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, which was held online during February 4-6, 2021.A total of 72 full and 99 short papers were carefully reviewed and selected for the conference from a total of 298 submissions; 17 selected full papers are included in this book. They were organized in topical sections named agents and artificial intelligence.
Next Generation Arithmetic
This book constitutes the refereed proceedings of the Third International Conference on Next Generation Arithmetic, CoNGA 2022, which was held in Singapore, during March 1-3, 2022. The 8 full papers included in this book were carefully reviewed and selected from 12 submissions. They deal with emerging technologies for computer arithmetic focusing on the demands of both AI and high-performance computing.
Journal on Policy and Complex Systems
This issues contents includes: Editor's LetterPercy Venegas Modeling NFT Investor Behavior Using Belief DissensusFernand Gobet and Percy Venegas Modelling & Simulation of a Rivet Shaving Process for the Protection of the Aerospace Industry Against Cyber-threats Martin Praddaude, Nicolas Hogrel, Matthieu Gay, Ulrike Baumann, and Adrien B矇cue Complex Simulation Workflows in Containerized High-Performance EnvironmentVladimr Visňovsk羸, Viktoria Spis獺kov獺, Jana Hozzov獺, Jaroslav Olha, Dalibor Trapl, Vojtech Spiwok, Lukas Hejtm獺nek, and Ales Křenek Augmented Reality Implementation for Comfortable Adaptation of Disabled Personnel to the Production WorkplaceOleg Surnin, Pavel Sitnikov, Alexandr Gubinkiy, Alexandr Dorofeev, Tatiana Nikiforova, Arkadiy Krivosheev, Vladimir Zemtsov, and Anton Ivaschenko Designing an Emergency Information System for an Emergency Information System for Catastrophic Natural SituationsK. Papatheodosiou and C.Angeli A Return to "A Complexity Context to Classroom Interactions and Climate Impact on Achievement"Joseph Cochran and Liz Johnson
Web Data APIs for Knowledge Graphs
This book describes a set of methods, architectures, and tools to extend the data pipeline at the disposal of developers when they need to publish and consume data from Knowledge Graphs (graph-structured knowledge bases that describe the entities and relations within a domain in a semantically meaningful way) using SPARQL, Web APIs, and JSON. To do so, it focuses on the paradigmatic cases of two middleware software packages, grlc and SPARQL Transformer, which automatically build and run SPARQL-based REST APIs and allow the specification of JSON schema results, respectively. The authors highlight the underlying principles behind these technologies--query management, declarative languages, new levels of indirection, abstraction layers, and separation of concerns--, explain their practical usage, and describe their penetration in research projects and industry. The book, therefore, serves a double purpose: to provide a sound and technical description of tools and methods at the disposal ofpublishers and developers to quickly deploy and consume Web Data APIs on top of Knowledge Graphs; and to propose an extensible and heterogeneous Knowledge Graph access infrastructure that accommodates a growing ecosystem of querying paradigms.
Graph Transformation
This book constitutes the refereed proceedings of the 15th International Conference on Graph Transformation, ICGT 2022, which took place Nantes, France in July 2022.The 10 full papers and 1 tool paper presented in this book were carefully reviewed and selected from 19 submissions. The conference focuses on describing new unpublished contributions in the theory and applications of graph transformation as well as tool presentation papers that demonstrate main new features and functionalities of graph-based tools.
Foundations of Scalable Systems
In many systems, scalability becomes the primary driver as the user base grows. Attractive features and high utility breed success, which brings more requests to handle and more data to manage. But organizations reach a tipping point when design decisions that made sense under light loads suddenly become technical debt. This practical book covers design approaches and technologies that make it possible to scale an application quickly and cost-effectively. Author Ian Gorton takes software architects and developers through the foundational principles of distributed systems. You'll explore the essential ingredients of scalable solutions, including replication, state management, load balancing, and caching. Specific chapters focus on the implications of scalability for databases, microservices, and event-based streaming systems. You will focus on: Foundations of scalable systems: Learn basic design principles of scalability, its costs, and architectural tradeoffs Designing scalable services: Dive into service design, caching, asynchronous messaging, serverless processing, and microservices Designing scalable data systems: Learn data system fundamentals, NoSQL databases, and eventual consistency versus strong consistency Designing scalable streaming systems: Explore stream processing systems and scalable event-driven processing
In-Memory Analytics with Apache Arrow
Process tabular data and build high-performance query engines on modern CPUs and GPUs using Apache Arrow, a standardized language-independent memory format, for optimal performanceKey Features: - Learn about Apache Arrow's data types and interoperability with pandas and Parquet- Work with Apache Arrow Flight RPC, Compute, and Dataset APIs to produce and consume tabular data- Reviewed, contributed, and supported by Dremio, the co-creator of Apache ArrowBook Description: Apache Arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily.In-Memory Analytics with Apache Arrow begins with a quick overview of the Apache Arrow format, before moving on to helping you to understand Arrow's versatility and benefits as you walk through a variety of real-world use cases. You'll cover key tasks such as enhancing data science workflows with Arrow, using Arrow and Apache Parquet with Apache Spark and Jupyter for better performance and hassle-free data translation, as well as working with Perspective, an open source interactive graphical and tabular analysis tool for browsers. As you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between Arrow, Parquet, Feather, Protobuf, Flatbuffers, JSON, and CSV. In addition to understanding the basic structure of the Arrow Flight and Flight SQL protocols, you'll learn about Dremio's usage of Apache Arrow to enhance SQL analytics and discover how Arrow can be used in web-based browser apps. Finally, you'll get to grips with the upcoming features of Arrow to help you stay ahead of the curve.By the end of this book, you will have all the building blocks to create useful, efficient, and powerful analytical services and utilities with Apache Arrow.What You Will Learn: - Use Apache Arrow libraries to access data files both locally and in the cloud- Understand the zero-copy elements of the Apache Arrow format- Improve read performance by memory-mapping files with Apache Arrow- Produce or consume Apache Arrow data efficiently using a C API- Use the Apache Arrow Compute APIs to perform complex operations- Create Arrow Flight servers and clients for transferring data quickly- Build the Arrow libraries locally and contribute back to the communityWho this book is for: This book is for developers, data analysts, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. This book will also be useful for any engineers who are working on building utilities for data analytics and query engines, or otherwise working with tabular data, regardless of the programming language. Some familiarity with basic concepts of data analysis will help you to get the most out of this book but isn't required. Code examples are provided in the C++, Go, and Python programming languages.Table of Contents- Getting Started with Apache Arrow- Working with Key Arrow Specifications- Data Science with Apache Arrow- Format and Memory Handling- Crossing the Language Barrier with the Arrow C Data API- Leveraging the Arrow Compute APIs- Using the Arrow Datasets API- Exploring Apache Arrow Flight RPC- Powered By Apache Arrow- How to Leave Your Mark on Arrow- Future Development and Plans
Edge-Of-Things in Personalized Healthcare Support Systems
Edge-of-Things in Personalized Healthcare Support Systems discusses and explores state-of-the-art technology developments in storage and sharing of personal healthcare records in a secure manner that is globally distributed to incorporate best healthcare practices. The book presents research into the identification of specialization and expertise among healthcare professionals, the sharing of records over the cloud, access controls and rights of shared documents, document privacy, as well as edge computing techniques which help to identify causes and develop treatments for human disease. The book aims to advance personal healthcare, medical diagnosis, and treatment by applying IoT, cloud, and edge computing technologies in association with effective data analytics.
Reversible Computation
This book constitutes the refereed proceedings of the 14th International Conference on Reversible Computation, RC 2022, which was held in Urbino, Italy, during July 5-6, 2021. The 10 full papers and 6 short papers included in this book were carefully reviewed and selected from 20 submissions. They were organized in topical sections named: Reversible and Quantum Circuits; Applications of quantum Computing; Foundations and Applications.
Time Series Analysis with Python Cookbook
Perform time series analysis and forecasting confidently with this Python code bank and reference manualKey Features: - Explore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms- Learn different techniques for evaluating, diagnosing, and optimizing your models- Work with a variety of complex data with trends, multiple seasonal patterns, and irregularitiesBook Description: Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting.This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you'll work with ML and DL models using TensorFlow and PyTorch.Finally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.What You Will Learn: - Understand what makes time series data different from other data- Apply various imputation and interpolation strategies for missing data- Implement different models for univariate and multivariate time series- Use different deep learning libraries such as TensorFlow, Keras, and PyTorch- Plot interactive time series visualizations using hvPlot- Explore state-space models and the unobserved components model (UCM)- Detect anomalies using statistical and machine learning methods- Forecast complex time series with multiple seasonal patternsWho this book is for: This book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.Table of Contents- Getting Started with Time Series Analysis- Reading Time Series Data from Files- Reading Time Series Data from Databases- Persisting Time Series Data to Files- Persisting Time Series Data to Databases- Working with Date and Time in Python- Handling Missing Data- Outlier Detection Using Statistical Methods- WExploratory Data Analysis and Diagnosis- Building Univariate Time Series Models Using Statistical Methods- Additional Statistical Modeling Techniques for Time Series- Forecasting Using Supervised Machine Learning- Deep Learning for Time Series Forecasting- Outlier Detection Using Unsupervised Machine Learning- Advanced Techniques for Complex Time Series
Engineering Psychology and Cognitive Ergonomics
This book constitutes the refereed proceedings of the 19th International Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2022, held as part of the 23rd International Conference, HCI International 2022, which was held virtually in June/July 2022. The total of 1271 papers and 275 posters included in the HCII 2022 proceedings was carefully reviewed and selected from 5487 submissions. The EPCE 2022 proceedings covers subjects such as advances in applied cognitive psychology that underpin the theory, measurement and methodologies behind the development of human-machine systems. Cognitive Ergonomics describes advances in the design and development of user interfaces.
Combinatorial Algorithms
This book constitutes the refereed proceedings of the 33rd International Workshop on Combinatorial Algorithms, IWOCA 2022, which took place as a hybrid event in Trier, Germany, during June 7-9, 2022.The 35 papers presented in these proceedings were carefully reviewed and selected from 86 submissions. They deal with diverse topics related to combinatorial algorithms, such as algorithms and data structures; algorithmic and combinatorical aspects of cryptography and information security; algorithmic game theory and complexity of games; approximation algorithms; complexity theory; combinatorics and graph theory; combinatorial generation, enumeration and counting; combinatorial optimization; combinatorics of words; computational biology; computational geometry; decompositions and combinatorial designs; distributed and network algorithms; experimental combinatorics; fine-grained complexity; graph algorithms and modelling with graphs; graph drawingand graph labelling; network theory and temporal graphs; quantum computing and algorithms for quantum computers; online algorithms; parameterized and exact algorithms; probabilistic andrandomized algorithms; and streaming algorithms.
Applying Reinforcement Learning on Real-World Data with Practical Examples in Python
Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach super-human performance, and continually adapt to changing conditions. This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, stock trading, supply chain optimization, and plant control. However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the setup is already provided. Furthermore, experimentation may be completed for an almost limitless number of attempts risk-free. In many real-life tasks, applying reinforcement learning is not as simple as (1) data is not in the correct form for reinforcement learning, (2) data is scarce, and (3) automation has limitations in the real-world. Therefore, this book is written to help academics, domain specialists, and data enthusiast alike to understand the basic principles of applying reinforcement learning to real-world problems. This is achieved by focusing on the process of taking practical examples and modeling standard data into the correct form required to then apply basic agents. To further assist with readers gaining a deep and grounded understanding of the approaches, the book shows hand-calculated examples in full and then how this can be achieved in a more automated manner with code. For decision makers who are interested in reinforcement learning as a solution but are not technically proficient we include simple, non-technical examples in the introduction and case studies section. These provide context of what reinforcement learning offer but also the challenges and risks associated with applying it in practice. Specifically, the book illustrates the differences between reinforcement learning and other machine learning approaches as well as how well-known companies have found success using the approach to their problems.
Demystifying OWL for the Enterprise
After a slow incubation period of nearly 15 years, a large and growing number of organizations now have one or more projects using the Semantic Web stack of technologies. The Web Ontology Language (OWL) is an essential ingredient in this stack, and the need for ontologists is increasing faster than the number and variety of available resources for learning OWL. This is especially true for the primary target audience for this book: modelers who want to build OWL ontologies for practical use in enterprise and government settings. The purpose of this book is to speed up the process of learning and mastering OWL. To that end, the focus is on the 30% of OWL that gets used 90% of the time. Others who may benefit from this book include technically oriented managers, semantic technology developers, undergraduate and post-graduate students, and finally, instructors looking for new ways to explain OWL. The book unfolds in a spiral manner, starting with the core ideas. Each subsequent cycle reinforces and expands on what has been learned in prior cycles and introduces new related ideas. Part 1 is a cook's tour of ontology and OWL, giving an informal overview of what things need to be said to build an ontology, followed by a detailed look at how to say them in OWL. This is illustrated using a healthcare example. Part 1 concludes with an explanation of some foundational ideas about meaning and semantics to prepare the reader for subsequent chapters. Part 2 goes into depth on properties and classes, which are the core of OWL. There are detailed descriptions of the main constructs that you are likely to need in every day modeling, including what inferences are sanctioned. Each is illustrated with real-world examples. Part 3 explains and illustrates how to put OWL into practice, using examples in healthcare, collateral, and financial transactions. A small ontology is described for each, along with some key inferences. Key limitations of OWL are identified, along with possible workarounds. The final chapter gives a variety of practical tips and guidelines to send the reader on their way.
Impossibility Results for Distributed Computing
To understand the power of distributed systems, it is necessary to understand their inherent limitations: what problems cannot be solved in particular systems, or without sufficient resources (such as time or space). This book presents key techniques for proving such impossibility results and applies them to a variety of different problems in a variety of different system models. Insights gained from these results are highlighted, aspects of a problem that make it difficult are isolated, features of an architecture that make it inadequate for solving certain problems efficiently are identified, and different system models are compared.
Multi-Core Cache Hierarchies
A key determinant of overall system performance and power dissipation is the cache hierarchy since access to off-chip memory consumes many more cycles and energy than on-chip accesses. In addition, multi-core processors are expected to place ever higher bandwidth demands on the memory system. All these issues make it important to avoid off-chip memory access by improving the efficiency of the on-chip cache. Future multi-core processors will have many large cache banks connected by a network and shared by many cores. Hence, many important problems must be solved: cache resources must be allocated across many cores, data must be placed in cache banks that are near the accessing core, and the most important data must be identified for retention. Finally, difficulties in scaling existing technologies require adapting to and exploiting new technology constraints. The book attempts a synthesis of recent cache research that has focused on innovations for multi-core processors. It is an excellent starting point for early-stage graduate students, researchers, and practitioners who wish to understand the landscape of recent cache research. The book is suitable as a reference for advanced computer architecture classes as well as for experienced researchers and VLSI engineers. Table of Contents: Basic Elements of Large Cache Design / Organizing Data in CMP Last Level Caches / Policies Impacting Cache Hit Rates / Interconnection Networks within Large Caches / Technology / Concluding Remarks
Designing and Building Enterprise Knowledge Graphs
This book is a guide to designing and building knowledge graphs from enterprise relational databases in practice.\ It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people. The content of this book is applicable to knowledge graphs being built either with property graph or RDF graph technologies. Knowledge graphs are fulfilling the vision of creating intelligent systems that integrate knowledge and data at large scale. Tech giants have adopted knowledge graphs for the foundation of next-generation enterprise data and metadata management, search, recommendation, analytics, intelligent agents, and more. We are now observing an increasing number of enterprises that seek to adopt knowledge graphs to develop a competitive edge. In order for enterprises to design and build knowledge graphs, they need to understand the critical data stored in relational databases. How can enterprises successfully adopt knowledge graphs to integrate data and knowledge, without boiling the ocean? This book provides the answers.
Knowledge Graphs
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques--based on statistics, graph analytics, machine learning, etc.--can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.
The Maximum Consensus Problem
Outlier-contaminated data is a fact of life in computer vision. For computer vision applications to perform reliably and accurately in practical settings, the processing of the input data must be conducted in a robust manner. In this context, the maximum consensus robust criterion plays a critical role by allowing the quantity of interest to be estimated from noisy and outlier-prone visual measurements. The maximum consensus problem refers to the problem of optimizing the quantity of interest according to the maximum consensus criterion. This book provides an overview of the algorithms for performing this optimization. The emphasis is on the basic operation or "inner workings" of the algorithms, and on their mathematical characteristics in terms of optimality and efficiency. The applicability of the techniques to common computer vision tasks is also highlighted. By collecting existing techniques in a single article, this book aims to trigger further developments in this theoretically interesting and practically important area.
Data Profiling
Data profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More complex types of metadata are statements about multiple columns and their correlation, such as candidate keys, functional dependencies, and other types of dependencies. This book provides a classification of the various types of profilable metadata, discusses popular data profiling tasks, and surveys state-of-the-art profiling algorithms. While most of the book focuses on tasks and algorithms for relational data profiling, we also briefly discuss systems and techniques for profiling non-relational data such as graphs and text. We conclude with a discussion of data profiling challenges and directions for future work in this area.
Data Engineering with Alteryx
Build and deploy data pipelines with Alteryx by applying practical DataOps principlesKey Features: Learn DataOps principles to build data pipelines with AlteryxBuild robust data pipelines with Alteryx DesignerUse Alteryx Server and Alteryx Connect to share and deploy your data pipelinesBook Description: Alteryx is a GUI-based development platform for data analytic applications.Data Engineering with Alteryx will help you leverage Alteryx's code-free aspects which increase development speed while still enabling you to make the most of the code-based skills you have.This book will teach you the principles of DataOps and how they can be used with the Alteryx software stack. You'll build data pipelines with Alteryx Designer and incorporate the error handling and data validation needed for reliable datasets. Next, you'll take the data pipeline from raw data, transform it into a robust dataset, and publish it to Alteryx Server following a continuous integration process.By the end of this Alteryx book, you'll be able to build systems for validating datasets, monitoring workflow performance, managing access, and promoting the use of your data sources.What You Will Learn: Build a working pipeline to integrate an external data sourceDevelop monitoring processes for the pipeline exampleUnderstand and apply DataOps principles to an Alteryx data pipelineGain skills for data engineering with the Alteryx software stackWork with spatial analytics and machine learning techniques in an Alteryx workflow Explore Alteryx workflow deployment strategies using metadata validation and continuous integrationOrganize content on Alteryx Server and secure user accessWho this book is for: If you're a data engineer, data scientist, or data analyst who wants to set up a reliable process for developing data pipelines using Alteryx, this book is for you. You'll also find this book useful if you are trying to make the development and deployment of datasets more robust by following the DataOps principles. Familiarity with Alteryx products will be helpful but is not necessary.
Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators
Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators deals with the modeling and control of parallel robots. The book's content will benefit students, researchers and engineers in robotics by providing a simplified methodology to obtain the dynamic model of parallel robots with a delta-type architecture. Moreover, this methodology is compatible with the real-time implementation of model-based and robust control schemes. And, it can easily extend the proposed robust control solutions to other robotic architectures.
Learning Google Analytics
Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Rather than simply reporting what has happened, GA4's new cloud integrations enable more data activation, linking 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 the guidance you need to implement them. You'll learn: How Google Cloud integrates with GA4 The potential use cases that GA4 integrations can enable Skills and resources needed to create GA4 integrations How much GA4 data capture is necessary to enable use cases The process of designing dataflows from strategy through data storage, modeling, and activation How to adapt the use cases to fit your business needs
Blockchain Technology for Emerging Applications
Blockchain Technology for Emerging Applications: A Comprehensive Approach explores recent theories and applications of the execution of blockchain technology. Chapters look at a wide range of application areas, including healthcare, digital physical frameworks, web of-things, smart transportation frameworks, interruption identification frameworks, ballot-casting, architecture, smart urban communities, and digital rights administration. The book addresses the engineering, plan objectives, difficulties, constraints, and potential answers for blockchain-based frameworks. It also looks at blockchain-based design perspectives of these intelligent architectures for evaluating and interpreting real-world trends. Chapters expand on different models which have shown considerable success in dealing with an extensive range of applications, including their ability to extract complex hidden features and learn efficient representation in unsupervised environments for blockchain security pattern analysis.
Wearable Sensing and Intelligent Data Analysis for Respiratory Management
Wearable Sensing and Intelligent Data Analysis for Respiratory Management highlights the use of wearable sensing and intelligent data analysis algorithms for respiratory function management, offering several potential and substantial clinical benefits. The book allows for the early detection of respiratory exacerbations in patients with chronic respiratory diseases, allowing earlier and, therefore, more effective treatment. As such, the problem of continuous, non-invasive, remote and real-time monitoring of such patients needs increasing attention from the scientific community as these systems have the potential for substantial clinical benefits, promoting P4 medicine (personalized, participative, predictive and preventive). Wearable and portable systems with sensing technology and automated analysis of respiratory sounds and pulmonary images are some of the problems that are the subject of current research efforts, hence this book is an ideal resource on the topics discussed.
Data Democratization with Domo
Overcome data challenges at record speed and cloud-scale that optimize businesses by transforming raw data into dashboards and apps which democratize data consumption, supercharging results with the cloud-based solution, DomoKey Features: Acquire data and automate data pipelines quickly for any data volume, variety, and velocityPresent relevant stories in dashboards and custom apps that drive favorable outcomes using DomoShare information securely and govern content including Domo content embedded in other toolsBook Description: Domo is a power-packed business intelligence (BI) platform that empowers organizations to track, analyze, and activate data in record time at cloud scale and performance.Data Democratization with Domo begins with an overview of the Domo ecosystem. You'll learn how to get data into the cloud with Domo data connectors and Workbench; profile datasets; use Magic ETL to transform data; work with in-memory data sculpting tools (Data Views and Beast Modes); create, edit, and link card visualizations; and create card drill paths using Domo Analyzer. Next, you'll discover options to distribute content with real-time updates using Domo Embed and digital wallboards. As you advance, you'll understand how to use alerts and webhooks to drive automated actions. You'll also build and deploy a custom app to the Domo Appstore and find out how to code Python apps, use Jupyter Notebooks, and insert R custom models. Furthermore, you'll learn how to use Auto ML to automatically evaluate dozens of models for the best fit using SageMaker and produce a predictive model as well as use Python and the Domo Command Line Interface tool to extend Domo. Finally, you'll learn how to govern and secure the entire Domo platform.By the end of this book, you'll have gained the skills you need to become a successful Domo master.What You Will Learn: Understand the Domo cloud data warehouse architecture and platformAcquire data with Connectors, Workbench, and Federated QueriesSculpt data using no-code Magic ETL, Data Views, and Beast ModesProfile data with the Data Dictionary, Data Profile, and Usage toolsUse a storytelling pattern to create dashboards with Domo StoriesCreate, share, and monitor custom alerts activated using webhooksCreate custom Domo apps, use the Domo CLI, and code with the Python APIAutomate model operations with Python programming and R scriptingWho this book is for: This book is for BI developers, ETL developers, and Domo users looking for a comprehensive, end-to-end guide to exploring Domo features for BI. Chief data officers, data strategists, architects, and BI managers interested in a new paradigm for integrated cloud data storage, data transformation, storytelling, content distribution, custom app development, governance, and security will find this book useful. Business analysts seeking new ways to tell relevant stories to shape business performance will also benefit from this book. A basic understanding of Domo will be helpful.
Data Forecasting and Segmentation Using Microsoft Excel
Perform time series forecasts, linear prediction, and data segmentation with no-code Excel machine learningKey Features: Segment data, regression predictions, and time series forecasts without writing any codeGroup multiple variables with K-means using Excel plugin without programmingBuild, validate, and predict with a multiple linear regression model and time series forecastsBook Description: Data Forecasting and Segmentation Using Microsoft Excel guides you through basic statistics to test whether your data can be used to perform regression predictions and time series forecasts. The exercises covered in this book use real-life data from Kaggle, such as demand for seasonal air tickets and credit card fraud detection.You'll learn how to apply the grouping K-means algorithm, which helps you find segments of your data that are impossible to see with other analyses, such as business intelligence (BI) and pivot analysis. By analyzing groups returned by K-means, you'll be able to detect outliers that could indicate possible fraud or a bad function in network packets.By the end of this Microsoft Excel book, you'll be able to use the classification algorithm to group data with different variables. You'll also be able to train linear and time series models to perform predictions and forecasts based on past data.What You Will Learn: Understand why machine learning is important for classifying data segmentationFocus on basic statistics tests for regression variable dependencyTest time series autocorrelation to build a useful forecastUse Excel add-ins to run K-means without programmingAnalyze segment outliers for possible data anomalies and fraudBuild, train, and validate multiple regression models and time series forecastsWho this book is for: This book is for data and business analysts as well as data science professionals. MIS, finance, and auditing professionals working with MS Excel will also find this book beneficial.
The DevOps Career Handbook
Explore the diverse DevOps career paths and prepare for each stage of the interview process with collective wisdom from DevOps experts and interviews with DevOps PractitionersKey Features: Navigate the many career opportunities in the field of DevOpsDiscover proven tips and tricks from industry experts for every step of the DevOps interviewSave both time and money by avoiding common mistakes in your interviewsBook Description: DevOps is a set of practices that make up a culture, and practicing DevOps methods can make developers more productive and easier to work with. The DevOps Career Handbook is filled with hundreds of tips and tricks from experts regarding every step of the interview process, helping you save time and money by steering clear of avoidable mistakes.You'll learn about the various career paths available in the field of DevOps, before acquiring the essential skills needed to begin working as a DevOps professional. If you are already a DevOps engineer, this book will help you to gain advanced skills to become a DevOps specialist. After getting to grips with the basics, you'll discover tips and tricks for preparing your resume and online profiles and find out how to build long-lasting relationships with the recruiters. Finally, you'll read through interviews which will give you an insight into a career in DevOps from the viewpoint of individuals at different career levels.By the end of this DevOps book, you'll gain a solid understanding of what DevOps is, the various DevOps career paths, and how to prepare for your interview.What You Will Learn: Understand various roles and career paths for DevOps practitionersDiscover proven techniques to stand out in the application processPrepare for the many stages of your interview, from the phone screen to taking the technical challenge and then the onsite interviewNetwork effectively to help your career move in the right directionTailor your resume to specific DevOps rolesDiscover how to negotiate after you've been extended an offerWho this book is for: This book is for DevOps professionals looking to take the next step in their career, engineers looking to make a career switch, technology managers who want to understand the complete picture of the DevOps landscape, and anyone interested in incorporating DevOps into their tech journey.
Logic and Language Models for Computer Science (Fourth Edition)
This unique compendium highlights the theory of computation, particularly logic and automata theory. Special emphasis is on computer science applications including loop invariants, program correctness, logic programming and algorithmic proof techniques.This innovative volume differs from standard textbooks, by building on concepts in a different order, using fewer theorems with simpler proofs. It has added many new examples, problems and answers. It can be used as an undergraduate text at most universities.
