Pattern-Based Constraint Satisfaction and Logic Puzzles (Third Edition)
"Pattern-Based Constraint Satisfaction and Logic Puzzles (Third Edition)" develops a pure logic, pattern-based perspective of solving the finite Constraint Satisfaction Problem (CSP), with emphasis on finding the "simplest" solution. Different ways of reasoning with the constraints are formalised by various families of "resolution rules", each of them carrying its own notion of simplicity. A large part of the book illustrates the power of the approach by applying it to various popular logic puzzles. It provides a unified view of how to model and solve them, even though they involve very different types of constraints: obvious symmetric ones in Sudoku, non-symmetric but transitive ones in Futoshiki, topological and geometric ones in Map colouring, Numbrix and Hidato, non-binary arithmetic ones in Kakuro and both non-binary and non-local ones in Slitherlink. It also shows that the most familiar techniques for these puzzles can be understood as mere application-specific presentations of the general rules. A free companion software (CSP-Rules-V2.1) implementing all the rules and above-mentioned applications is available on GitHub under the GPL license.
Strategic Monoliths and Microservices
Make Software Architecture Choices That Maximize Value and Innovation "[Vernon and Jaskula] provide insights, tools, proven best practices, and architecture styles both from the business and engineering viewpoint. . . . This book deserves to become a must-read for practicing software engineers, executives as well as senior managers." --Michael Stal, Certified Senior Software Architect, Siemens Technology Strategic Monoliths and Microservices helps business decision-makers and technical team members clearly understand their strategic problems through collaboration and identify optimal architectural approaches, whether the approach is distributed microservices, well-modularized monoliths, or coarser-grained services partway between the two. Leading software architecture experts Vaughn Vernon and Tomasz Jaskula show how to make balanced architectural decisions based on need and purpose, rather than hype, so you can promote value and innovation, deliver more evolvable systems, and avoid costly mistakes. Using realistic examples, they show how to construct well-designed monoliths that are maintainable and extensible, and how to gradually redesign and reimplement even the most tangled legacy systems into truly effective microservices. Link software architecture planning to business innovation and digital transformation Overcome communication problems to promote experimentation and discovery-based innovation Master practices that support your value-generating goals and help you invest more strategically Compare architectural styles that can lead to versatile, adaptable applications and services Recognize when monoliths are your best option and how best to architect, design, and implement them Learn when to move monoliths to microservices and how to do it, whether they're modularized or a "Big Ball of Mud" Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Towards Autonomous Robotic Systems
The volume LNAI 13054 constitutes the refereed proceedings of the 22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021, held in Lincoln, UK, in September 2021.*The 45 full papers were carefully reviewed and selected from 66 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: artificial intelligence; mechatronics; image processing and computer vision; special purpose and application-based systems; user interfaces and human computer interaction.* The conference was held virtually due to the COVID-19 pandemic.
Machine Learning for Time-Series with Python
Become proficient in deriving insights from time-series data and analyzing a model's performanceKey Features: Explore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time-series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description: Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.What You Will Learn: Understand the main classes of time-series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models like Gaussian processes and transformers and state-of-the-art machine learning modelsBecome familiar with many libraries like prophet, xgboost, and TensorFlowWho this book is for: This book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.
Conquering R Basics
This book and its accompanying video library attempts to tackle a colossal challenge. It proposes to define what is otherwise an ambiguous interpretation of a basic skill set underlying a global technology. That technology is R. The R basic skill set proposed in this book comprises the following targeted elements: (93) indispensable R functions are identified and explained, all of which can be used in a vast array of data projects.The concept of the Data Narrative is introduced and explained.Data is programmatically connected with commonly used plot visualizations, explained with specificity and supporting context.The application of color in plot development is introduced and explained.
Semi-Custom IC Design and VLSI
The contents of this book were first presented as a series of lectures at the first IEE Vacation School on Semi-Custom IC Design and VLSI held at the University of Edinburgh on 4-8 July 1983. The earlier chapters provide an introduction to silicon IC technology and include descriptions of the various processing techniques employed in the manufacture of microelectronic components. Different types of semi-custom IC are then reviewed and the factors that have to be considered in choosing a semi-custom technique are examined in detail. Logic design is next presented as an activity that is best carried out at a higher level of abstraction than the customary/logic gate level by using the algorithmic state machine (ASM) method. In the sections that follow, computer aids to design and design automation tools are introduced as essential requirements for the rapid and error-free design of semicustom ICs. Testing strategies and the need to design for testability are also covered in some detail. Although a heavy emphasis is placed on the design of semi-custom ICs, consideration is also given to the ways in which custom VLSI circuits will be designed in future. The merits of the programmable logic array (PLA) as a VLSI building-block are put forward, and the silicon compiler is presented as possibly the ultimate 'semi-custom' technique. The authors who have contributed to this volume are specialists in their field who can claim many years of experience either in the microelectronics industry or in universities throughout the UK.
Aprendizaje Autom獺tico en Acci籀n
聶Est獺 buscando un libro fundamental para comenzar con los conceptos b獺sicos del aprendizaje autom獺tico? Mi libro le explicar獺 los conceptos b獺sicos de manera que sean f獺ciles de entender. Una vez que haya le穩do este libro, tendr獺 una s籀lida comprensi籀n de los principios b獺sicos que facilitar獺n el paso a un libro m獺s avanzado si desea obtener m獺s informaci籀n.
Simplifying Service Management with Consul
Understand the basics of the Consul server and client architecture, and learn how to apply Consul for dynamic and secure service discovery, communication, and network connectivity automationKey Features: Discover how Consul servers and clients operate to facilitate primary Consul use casesLearn how Consul dynamically and securely discovers and shares service data throughout the networkUtilize Consul to extend and secure network communications across multiple operating environments Book Description: Within the elastic and dynamic nature of cloud computing, efficient and accurate service discovery provides the cornerstone for all communications. HashiCorp Consul facilitates this service discovery efficiently and securely, independent of the operating environment. This book will help you build a solid understanding of both the concepts and applications of HashiCorp Consul.You'll begin by finding out what you can do with Consul, focusing on the conceptual views of configuration samples along with Terraform code to expedite lab environment and hands-on experimentation, which will enable you to apply Consul effectively in your everyday lives. As you advance, you'll learn how to set up your own Consul cluster and agents in a single datacenter or location and understand how Consul utilizes RAFT and GOSSIP protocols for communication. You'll also explore the practical applications of primary Consul use cases, including communication flows and configuration and code examples. With that knowledge, you'll extend Consul across datacenters to discuss the applicability of multiple regions, multiple clouds, and hybrid cloud environments.By the end of this Consul book, you will have the tools needed to create and operate your own Consul cluster and be able to facilitate your service discovery and communication.What You Will Learn: Deploy and configure a highly available multi-node Consul architectureImplement Consul service discovery across multiple servicesUtilize Consul to monitor and communicate service health statusConnect services securely across a range of environmentsLeverage your knowledge of the Consul service to automate network infrastructureExtend your Consul knowledge and connectivity across multiple environmentsWho this book is for: If you are a solutions architect, DevOps engineer, or anyone new to the cloud-native framework looking to get started with using Consul, then this book is for you. Knowledge of Terraform is helpful but not necessary. A basic understanding of networking and Kubernetes systems will help you get the most out of this book.
Serverless Analytics with Amazon Athena
Get more from your data with Amazon Athena's ease-of-use, interactive performance, and pay-per-query pricingKey Features: Explore the promising capabilities of Amazon Athena and Athena's Query Federation SDKUse Athena to prepare data for common machine learning activitiesCover best practices for setting up connectivity between your application and Athena and security considerationsBook Description: Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using SQL, without needing to manage any infrastructure.This book begins with an overview of the serverless analytics experience offered by Athena and teaches you how to build and tune an S3 Data Lake using Athena, including how to structure your tables using open-source file formats like Parquet. You'll learn how to build, secure, and connect to a data lake with Athena and Lake Formation. Next, you'll cover key tasks such as ad hoc data analysis, working with ETL pipelines, monitoring and alerting KPI breaches using CloudWatch Metrics, running customizable connectors with AWS Lambda, and more. Moving on, you'll work through easy integrations, troubleshooting and tuning common Athena issues, and the most common reasons for query failure. You will also review tips to help diagnose and correct failing queries in your pursuit of operational excellence. Finally, you'll explore advanced concepts such as Athena Query Federation and Athena ML to generate powerful insights without needing to touch a single server.By the end of this book, you'll be able to build and use a data lake with Amazon Athena to add data-driven features to your app and perform the kind of ad hoc data analysis that often precedes many of today's ML modeling exercises.What You Will Learn: Secure and manage the cost of querying your dataUse Athena ML and User Defined Functions (UDFs) to add advanced features to your reportsWrite your own Athena Connector to integrate with a custom data sourceDiscover your datasets on S3 using AWS Glue CrawlersIntegrate Amazon Athena into your applicationsSetup Identity and Access Management (IAM) policies to limit access to tables and databases in Glue Data CatalogAdd an Amazon SageMaker Notebook to your Athena queriesGet to grips with using Athena for ETL pipelinesWho this book is for: Business intelligence (BI) analysts, application developers, and system administrators who are looking to generate insights from an ever-growing sea of data while controlling costs and limiting operational burden, will find this book helpful. Basic SQL knowledge is expected to make the most out of this book.
Modern Problems of Robotics
This book constitutes the post-conference proceedings of the 2nd International Conference on Modern Problems of Robotics, MPoR 2020, held in Moscow, Russia, in March 2020.The 16 revised full papers were carefully reviewed and selected from 21 submissions. The volume includes the following topical sections: Collaborative Robotic Systems, Robotic Systems Design and Simulation, and Robots Control. The papers are devoted to the most interesting today's investigations in Robotics, such as the problems of the human-robot interaction, the problems of robot design and simulation, and the problems of robot and robotic complexes control.
A Greater Foundation for Machine Learning Engineering
This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Na簿ve Bayes, Na簿ve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.
A Greater Foundation for Machine Learning Engineering
This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Na簿ve Bayes, Na簿ve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.
Practical Data Science with Python
Learn to effectively manage data and execute data science projects from start to finish using PythonKey Features: Understand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description: Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.By the end of the book, you should be able to comfortably use Python for basic data science projects and should have skills to execute the data science process on any data source.What You Will Learn: Use Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modelling, including classic statistical models (e.g., t-tests), and essential machine learning (ML) algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different ML methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for: The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor's, Master's, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
Essential PySpark for Scalable Data Analytics
Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scaleKey Features: Discover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description: Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework.Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that enable you to gain insights much faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability and performance to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas.By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems.What You Will Learn: Understand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for: This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.
Logical Data Modeling
Introduces the fundamentals of the Entity-Relationship Model, lays out a common set of principles to follow when modeling, gives detailed guidance on building a logical data model for real-world systems development projects, and follows a best practices approach-presents the lessons learned from dozens of experienced data modelers over decades of data modeling.In addition, this book is useful for the experienced data modeler who wants to expand his or her knowledge of the E-R Model or learn some tips and tricks of the trade.
Machine Learning with Amazon SageMaker Cookbook
A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMakerKey Features: Perform ML experiments with built-in and custom algorithms in SageMakerExplore proven solutions when working with TensorFlow, PyTorch, Hugging Face Transformers, and scikit-learnUse the different features and capabilities of SageMaker to automate relevant ML processesBook Description: Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.What You Will Learn: Train and deploy NLP, time series forecasting, and computer vision models to solve different business problemsPush the limits of customization in SageMaker using custom container imagesUse AutoML capabilities with SageMaker Autopilot to create high-quality modelsWork with effective data analysis and preparation techniquesExplore solutions for debugging and managing ML experiments and deploymentsDeal with bias detection and ML explainability requirements using SageMaker ClarifyAutomate intermediate and complex deployments and workflows using a variety of solutionsWho this book is for: This book is for developers, data scientists, and machine learning practitioners interested in using Amazon SageMaker to build, analyze, and deploy machine learning models with 80 step-by-step recipes. All you need is an AWS account to get things running. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
Data Engineering with Apache Spark, Delta Lake, and Lakehouse
Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big dataKey Features: Become well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description: In the world of ever-changing data and ever-evolving schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on.Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way.By the end of this data engineering book, you'll have learned how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks.What You Will Learn: Discover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for: This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.
Power Query Cookbook
Leverage your source data from hundreds of different connections, perform millions of different transformations, and easily manage highly complex data lifecycles with Power QueryKey Features: Collect, combine, and transform data using Power Query's data connectivity and data preparation featuresOvercome the problems faced while accessing data from multiple sources and reshape it to meet your business requirementsExplore how the M language can be used to write your own customized solutionsBook Description: Power Query is a data preparation tool that enables data engineers and business users to connect, reshape, enrich, and transform their data to facilitate relevant business insights and analysis. With Power Query's wide range of features, you can perform no-code transformations and complex M code functions at the same time to get the most out of your data.This Power Query book will help you to connect to data sources, achieve intuitive transformations, and get to grips with preparation practices. Starting with a general overview of Power Query and what it can do, the book advances to cover more complex topics such as M code and performance optimization. You'll learn how to extend these capabilities by gradually stepping away from the Power Query GUI and into the M programming language. Additionally, the book also shows you how to use Power Query Online within Power BI Dataflows.By the end of the book, you'll be able to leverage your source data, understand your data better, and enrich it with a full stack of no-code and custom features that you'll learn to design by yourself for your business requirements.What You Will Learn: Understand how to use Power Query to connect and explore dataExplore ways to reshape and enrich dataDiscover the potential of Power Query across the Microsoft platformBuild complex and custom transformationsUse M code to write new queries against data sourcesUse the Power Query Online tool within Power BI DataflowsImplement best practices such as reusing dataflows, optimizing expanding table operations, and field mappingWho this book is for: This book is for data analysts, BI developers, data engineers, and anyone looking for a desk reference guide to learn how Power Query can be used with different Microsoft products to handle data of varying complexity. Beginner-level knowledge of Power BI and the M Language will help you to get the best out of this book.
Data Science for Dummies
Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework - A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you'll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one's talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you're new to the data science field or already a decade in, you're sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company's data by picking up your copy today.
IBM Cloud Pak for Data
Build end-to-end AI solutions with IBM Cloud Pak for Data to operationalize AI on a secure platform based on cloud-native reliability, cost-effective multitenancy, and efficient resource managementKey Features: Explore data virtualization by accessing data in real time without moving itUnify the data and AI experience with the integrated end-to-end platformExplore the AI life cycle and learn to build, experiment, and operationalize trusted AI at scaleBook Description: Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services.You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects.By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise.What You Will Learn: Understand the importance of digital transformations and the role of data and AI platformsGet to grips with data architecture and its relevance in driving AI adoption using IBM's AI LadderUnderstand Cloud Pak for Data, its value proposition, capabilities, and unique differentiatorsDelve into the pricing, packaging, key use cases, and competitors of Cloud Pak for DataUse the Cloud Pak for Data ecosystem with premium IBM and third-party servicesDiscover IBM's vibrant ecosystem of proprietary, open-source, and third-party offerings from over 35 ISVsWho this book is for: This book is for data scientists, data stewards, developers, and data-focused business executives interested in learning about IBM's Cloud Pak for Data. Knowledge of technical concepts related to data science and familiarity with data analytics and AI initiatives at various levels of maturity are required to make the most of this book.
Artificial Intelligence and Data Science in Environmental Sensing
Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications.
Amazon SageMaker Best Practices
Overcome advanced challenges in building end-to-end ML solutions by leveraging the capabilities of Amazon SageMaker for developing and integrating ML models into productionKey Features: Learn best practices for all phases of building machine learning solutions - from data preparation to monitoring models in productionAutomate end-to-end machine learning workflows with Amazon SageMaker and related AWSDesign, architect, and operate machine learning workloads in the AWS CloudBook Description: Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions.By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.What You Will Learn: Perform data bias detection with AWS Data Wrangler and SageMaker ClarifySpeed up data processing with SageMaker Feature StoreOvercome labeling bias with SageMaker Ground TruthImprove training time with the monitoring and profiling capabilities of SageMaker DebuggerAddress the challenge of model deployment automation with CI/CD using the SageMaker model registryExplore SageMaker Neo for model optimizationImplement data and model quality monitoring with Amazon Model MonitorImprove training time and reduce costs with SageMaker data and model parallelismWho this book is for: This book is for expert data scientists responsible for building machine learning applications using Amazon SageMaker. Working knowledge of Amazon SageMaker, machine learning, deep learning, and experience using Jupyter Notebooks and Python is expected. Basic knowledge of AWS related to data, security, and monitoring will help you make the most of the book.
Building Data-Driven Applications with Danfo.js
Get hands-on with building data-driven applications using Danfo.js in combination with other data analysis tools and techniques Key Features: Build microservices to perform data transformation and ML model serving in JavaScriptExplore what Danfo.js is and how it helps with data analysis and data visualizationCombine Danfo.js and TensorFlow.js for machine learningBook Description: Most data analysts use Python and pandas for data processing for the convenience and performance these libraries provide. However, JavaScript developers have always wanted to use machine learning in the browser as well. This book focuses on how Danfo.js brings data processing, analysis, and ML tools to JavaScript developers and how to make the most of this library to build data-driven applications.Starting with an overview of modern JavaScript, you'll cover data analysis and transformation with Danfo.js and Dnotebook. The book then shows you how to load different datasets, combine and analyze them by performing operations such as handling missing values and string manipulations. You'll also get to grips with data plotting, visualization, aggregation, and group operations by combining Danfo.js with Plotly. As you advance, you'll create a no-code data analysis and handling system and create-react-app, react-table, react-chart, Draggable.js, and tailwindcss, and understand how to use TensorFlow.js and Danfo.js to build a recommendation system. Finally, you'll build a Twitter analytics dashboard powered by Danfo.js, Next.js, node-nlp, and Twit.js.By the end of this app development book, you'll be able to build and embed data analytics, visualization, and ML capabilities into any JavaScript app in server-side Node.js or the browser.What You Will Learn: Perform data experimentation and analysis with Danfo.js and DnotebookBuild machine learning applications using Danfo.js integrated with TensorFlow.jsConnect Danfo.js with popular database applications to aid data analysisCreate a no-code data analysis and handling system using internal librariesDevelop a recommendation system with Danfo.js and TensorFlow.jsBuild a Twitter analytics dashboard for sentiment analysis and other types of data insightsWho this book is for: This book is for data analysts, data scientists, and JavaScript developers who want to create data-driven applications in the JavaScript/Node.js environment. Intermediate-level knowledge of JavaScript programming and data science using pandas is expected.
The Art of Statistics
In this "important and comprehensive" guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life's biggest problems. The age of big data has made statistical literacy more important than ever. In The Art of Statistics, David Spiegelhalter shows how to apply statistical reasoning to real-world problems. Whether we're analyzing preventative medical screening or the terrible crime sprees of serial killers, Spiegelhalter teaches us how to clarify questions, assumptions, and expectations and, most importantly, how to interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to the power of data. "A call to arms for greater societal data literacy . . . a reminder that there are passionate, self-aware statisticians who can argue eloquently that their discipline is needed now more than ever." -- Financial Times
Azure Databricks Cookbook
Get to grips with building and productionizing end-to-end big data solutions in Azure and learn best practices for working with large datasetsKey Features: Integrate with Azure Synapse Analytics, Cosmos DB, and Azure HDInsight Kafka Cluster to scale and analyze your projects and build pipelinesUse Databricks SQL to run ad hoc queries on your data lake and create dashboardsProductionize a solution using CI/CD for deploying notebooks and Azure Databricks Service to various environmentsBook Description: Azure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse.The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You'll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you'll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD).By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps.What You Will Learn: Read and write data from and to various Azure resources and file formatsBuild a modern data warehouse with Delta Tables and Azure Synapse AnalyticsExplore jobs, stages, and tasks and see how Spark lazy evaluation worksHandle concurrent transactions and learn performance optimization in Delta tablesLearn Databricks SQL and create real-time dashboards in Databricks SQLIntegrate Azure DevOps for version control, deploying, and productionizing solutions with CI/CD pipelinesDiscover how to use RBAC and ACLs to restrict data accessBuild end-to-end data processing pipeline for near real-time data analyticsWho this book is for: This recipe-based book is for data scientists, data engineers, big data professionals, and machine learning engineers who want to perform data analytics on their applications. Prior experience of working with Apache Spark and Azure is necessary to get the most out of this book.
SGBD relationnels - Tome 1
Le tome 1 de cet ouvrage est consacr矇 ? la pr矇sentation des fondamentaux des SGBD relationnels. Il d矇crit, de fa癟on d矇taill矇e, comment la deuxi癡me g矇n矇ration de la technologie des bases de donn矇es, bas矇e sur le mod癡le relationnel de E.F. CODD, a r矇ussi ? prendre en compte les principales exigences, li矇es aux pr矇occupations des entreprises, pour pouvoir servir de support pour le stockage des donn矇es d'un syst癡me d'information, ? savoir: les exigences li矇es ? l'ind矇pendance des donn矇es, ? leur int矇grit矇, ? leur confidentialit矇, ? la gestion des acc癡s simultan矇s par plusieurs utilisateurs, ? leur s矇curit矇 en cas d'incident, aux performances et ? l'ad矇quation du langage d'interface permettant leur manipulation selon une approche relationnelle.Dans le discours, la pr矇sentation des concepts, des langages, des m矇thodes et des techniques est abondamment illustr矇e par des exemples en s'appuyant notamment sur le SGBD Oracle 12c. Cet ouvrage peut servir de support pour l'enseignement de la technologie des bases de donn矇es aussi bien ? un enseignant pour la pr矇paration de ses cours qu'? un apprenant pour l'approfondissement de son apprentissage.On y trouve: (1) une pr矇sentation des concepts du mod癡le relationnel servant de support ? l'organisation logique des donn矇es, (2) une pr矇sentation des langages abstraits de manipulation de ces donn矇es, d矇riv矇s de ces concepts en prenant appui sur la th矇orie des ensembles et sur la logique du premier ordre, (3) une pr矇sentation des techniques d'ing矇nierie d'une organisation logique des donn矇es, d矇riv矇es 矇galement des concepts du mod癡le relationnel, bas矇es sur la normalisation, ayant la capacit矇 de garantir le respect des contraintes d'int矇grit矇, d矇coulant des r癡gles de gestion relatives aux d矇pendances qui doivent 礙tre maintenues entre les donn矇es 矇l矇mentaires d'un syst癡me d'information.SQL, le langage d'interface normalis矇 des SGBD relationnels y est pr矇sent矇 en mettant en 矇vidence les apports du mod癡le relationnel qui en font d'une part, un puissant langage de d矇finition des donn矇es int矇grant nativement la sp矇cification des contraintes d'int矇grit矇 et de confidentialit矇 et d'autre part, un puissant langage abstrait de manipulation de ces donn矇es selon une approche dite relationnelle. Ceci permet de r矇pondre de fa癟on satisfaisante aux exigences li矇es ? l'int矇grit矇, ? la confidentialit矇 et ? l'ad矇quation de l'interface d'acc癡s pour la manipulation des donn矇es selon une approche relationnelle.Cette pr矇sentation inclut une pr矇sentation d矇taill矇e des extensions apport矇es ? SQL, pour en faire un langage complet de programmation, int矇grant nativement les fonctionnalit矇s pouvant permettre l'ex矇cution des ordres SQL de d矇marcation des transactions et de manipulation d'une base de donn矇es, en 矇vitant tout conflit d'imp矇dance li矇 ? une incompatibilit矇 entre les types de ce langage et les types du SGBD.Cette pr矇sentation inclut aussi une pr矇sentation d矇taill矇e des connecteurs de bases de donn矇es dont le r繫le est de permettre la manipulation d'une base de donn矇es ? l'aide de requ礙tes SQL ? partir d'un programme 矇crit dans un langage de programmation existant.La mani癡re dont les SGBD relationnels proc癡dent pour satisfaire aux exigences relatives ? l'ind矇pendance, aux performances, ? la gestion des acc癡s simultan矇s par plusieurs utilisateurs et ? la s矇curit矇 des donn矇es lors de la survenue d'un incident, y est abord矇e ? travers une pr矇sentation des techniques mises en oeuvre pour (1) l'impl矇mentation physique des donn矇es sur les supports de stockage, (2) la transformation des requ礙tes SQL en code ex矇cutable et (3) la gestion de la concurrence et des incidents.
Collaborative Design for Embedded Systems
One of the most significant challenges in the development of embedded and cyber-physical systems is the gap between the disciplines of software and control engineering. In a marketplace, where rapid innovation is essential, engineers from both disciplines need to be able to explore system designs collaboratively, allocating responsibilities to software and physical elements, and analyzing trade-offs between them.To this end, this book presents a framework that allows the very different kinds of design models - discrete-event (DE) models of software and continuous time (CT) models of the physical environment - to be analyzed and simulated jointly, based on common scenarios. The individual chapters provide introductions to both sides of this co-simulation technology, and give a step-by-step guide to the methodology for designing and analyzing co-models. They are grouped into three parts: Part I introduces the technical basis for collaborative modeling and simulation with the Crescendo technology. Part II continues with different methodological guidelines for creating co-models and analyzing them in different ways using case studies. Part III then delves into more advanced topics and looks into the potential future of this technology in the area of cyber-physical systems. Finally various appendices provide summaries of the VDM and 20-sim technologies, a number of valuable design patterns applicable for co-models, and an acronym list along with indices and references to other literature. By combining descriptions of the underlying theory with records of real engineers' experience in using the framework on a series of case studies the book appeals to scientists and practitioners alike. It is complemented by tools, examples, videos, and other material on www.crescendotool.org.Scientists/researchers and graduate students working in embedded and cyber-physical systems will learn the semantic foundations for collaborative modeling and simulation, as well as the current capabilities and limitations of methods and tools in this field. Practitioners will be able to develop an appreciation of the capabilities of the co-modeling techniques, to assess the benefits of more collaborative approaches to modeling and simulation, and will benefit from the included guidelines and modeling patterns.
Implementation of Smart Healthcare Systems Using Ai, Iot, and Blockchain
Implementation of Smart Healthcare Systems using AI, IoT, and Blockchain provides imperative research on the development of data fusion and analytics for healthcare and their implementation into current issues in a real-time environment. While highlighting IoT, bio-inspired computing, big data, and evolutionary programming, the book explores various concepts and theories of data fusion, IoT, and Big Data Analytics. It also investigates the challenges and methodologies required to integrate data from multiple heterogeneous sources, analytical platforms in healthcare sectors. This book is unique in the way that it provides useful insights into the implementation of a smart and intelligent healthcare system in a post-Covid-19 world using enabling technologies like Artificial Intelligence, Internet of Things, and blockchain in providing transparent, faster, secure and privacy preserved healthcare ecosystem for the masses.
Amazon Redshift Cookbook
Discover how to build a cloud-based data warehouse at petabyte-scale that is burstable and built to scale for end-to-end analytical solutionsKey Features: Discover how to translate familiar data warehousing concepts into Redshift implementationUse impressive Redshift features to optimize development, productionizing, and operations processesFind out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queriesBook Description: Amazon Redshift is a fully managed, petabyte-scale AWS cloud data warehousing service. It enables you to build new data warehouse workloads on AWS and migrate on-premises traditional data warehousing platforms to Redshift.This book on Amazon Redshift starts by focusing on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll then learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. Because of the massive amount of data involved in data warehousing, designing your database for analytical processing lets you take full advantage of Redshift's columnar architecture and managed services. As you advance, you'll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, which help minimize the operational efforts that you have to invest in managing regular ETL pipelines and ensure the timely and accurate refreshing of your data warehouse. Finally, you'll gain a clear understanding of Redshift use cases, data ingestion, data management, security, and scaling so that you can build a scalable data warehouse platform.By the end of this Redshift book, you'll be able to implement a Redshift-based data analytics solution and have understood the best practice solutions to commonly faced problems.What You Will Learn: Use Amazon Redshift to build petabyte-scale data warehouses that are agile at scaleIntegrate your data warehousing solution with a data lake using purpose-built features and services on AWSBuild end-to-end analytical solutions from data sourcing to consumption with the help of useful recipesLeverage Redshift's comprehensive security capabilities to meet the most demanding business requirementsFocus on architectural insights and rationale when using analytical recipesDiscover best practices for working with big data to operate a fully managed solutionWho this book is for: This book is for anyone involved in architecting, implementing, and optimizing an Amazon Redshift data warehouse, such as data warehouse developers, data analysts, database administrators, data engineers, and data scientists. Basic knowledge of data warehousing, database systems, and cloud concepts and familiarity with Redshift will be beneficial.
Developing Microservices Architecture on Microsoft Azure with Open Source Technologies
Deliver microservices architecture, step-by-step: from defining business problems through development, deployment, and monitoring Increasingly, organizations are modernizing application development by integrating open source technologies into a holistic architecture for delivering high-quality workloads to the cloud. This is a complete, step-by-step guide to building flexible microservices architecture by leveraging Microsoft Azure cloud services, together with key open source technologies such as Java, Node.JS, .NET Core and Angular. Through a realistic case study project, expert Microsoft engineers Ovais Mehboob Ahmed Khan and Arvind Chandaka guide you through every step of technical implementation required to achieve value: establishing end-to-end infrastructure, developing cloud-native applications, automating deployments, monitoring operations, and more. Microsoft engineers Ovais Mehboob Ahmed Khan and Arvind Chandaka show how to: Define application features and business requirements, and map them onto microservices using modeling techniques Design microservices solution architecture that enables high-quality workloads Develop an application front-end, and build microservices with open source technologies Leverage Azure Kubernetes Services for Docker container orchestration Use various patterns to build reliable and resilient microservices Enforce microservices app security, and use Azure AD B2C for user authentication/authorization Establish an API gateway that provides a unified "front door" to back-end microservices Set up continuous integration and deployment with Azure DevOps Monitor microservices with Azure Monitor and Azure Application Insights About This Book For everyone interested in developing microservices, including architects, engineers, and consultants Will help IT professionals build new applications, modernize existing systems, migrate workloads, improve app management, and more.
Machine Learning Engineering with MLflow
Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approachKey Features: Explore machine learning workflows for stating ML problems in a concise and clear manner using MLflowUse MLflow to iteratively develop a ML model and manage itDiscover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environmentBook Description: MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments.This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you'll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins.By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.What You Will Learn: Develop your machine learning project locally with MLflow's different featuresSet up a centralized MLflow tracking server to manage multiple MLflow experimentsCreate a model life cycle with MLflow by creating custom modelsUse feature streams to log model results with MLflowDevelop the complete training pipeline infrastructure using MLflow featuresSet up an inference-based API pipeline and batch pipeline in MLflowScale large volumes of data by integrating MLflow with high-performance big data librariesWho this book is for: This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.
Explainable and Transparent AI and Multi-Agent Systems
This book constitutes the proceedings of the Third International Workshop on Explainable, Transparent AI and Multi-Agent Systems, EXTRAAMAS 2021, which was held virtually due to the COVID-19 pandemic.The 19 long revised papers and 1 short contribution were carefully selected from 32 submissions. The papers are organized in the following topical sections: XAI & machine learning; XAI vision, understanding, deployment and evaluation; XAI applications; XAI logic and argumentation; decentralized and heterogeneous XAI.
Real-Time C++
Part I: Language Technologies for Real-Time C++.- 1. Getting Started with Real-Time C++.- 2. Working with a Real-Time C++ Program on a Board.- 3. An Easy Jump Start in Real-Time C++.- 4. Object-Oriented Techniques for Microcontrollers.- 5. C++ Templates for Microcontrollers.- 6. Optimized C++ Programming for Microcontrollers.- Part II: Components for Real-Time C++.- 7. Accessing Microcontroller Registers.- 8. The Right Start.- 9. Low-Level Hardware Drivers in C++.- 10. Custom Memory Management.- 11. C++ Multitasking.- Part III: Mathematics and Utilities for Real-Time C++.- 12. Floating-Point Mathematics.- 13. Fixed-Point Mathematics.- 14. High-Performance Digital Filters.- 15. C++ Utilities.- 16. Extending the C++ Standard Library and the STL.- 17. Using C-Language Code in C++.- Additional Reading.
Data Model Storytelling
What value does data modeling contribute to an organization, and how can that value be increased? Starting with analogies from Native American storytelling traditions and drawing on his experiences as an officer in Toastmasters (the International public speaking organization), Larry shows us how we can be data modeling Shamans. We can use data models to bring people together to solve business problems, reengineer inefficient business processes, forge agreements on data meanings, create new business opportunities, and promote data quality and reuse.Drawing from a host of disciplines, from storytelling to cognitive behavioral therapy, from landscaping to Human-Centered Design, from business process reengineering to domain-driven development, from Agile to object-oriented design, Larry weaves all these threads together into a compelling narrative of using data to get us successfully from where we are now to where we want to be!If you're a data professional, the bad news is that change is inevitable. But the good news is that change is not only possible, but fun! Larry's book shows how to navigate the shoals of business and technology change, from Agile development to NoSQL databases to Domain-Driven Development to microservices to cloud computing. The Journey starts here.
Business Value Of Information Technology In The 21st Century
By Providing Overall Leadership, Creating IT Business Value, Navigating New Technology, Implementing Innovation
Cloud Scale Analytics with Azure Data Services
A practical guide to implementing a scalable and fast state-of-the-art analytical data estateKey Features: Store and analyze data with enterprise-grade security and auditingPerform batch, streaming, and interactive analytics to optimize your big data solutions with easeDevelop and run parallel data processing programs using real-world enterprise scenariosBook Description: Azure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality.This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs.By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs.What You Will Learn: Implement data governance with Azure servicesUse integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure MonitorExplore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wranglingImplement networking with Synapse Analytics and Spark poolsCreate and run Spark jobs with Databricks clustersImplement streaming using Azure Functions, a serverless runtime environment on AzureExplore the predefined ML services in Azure and use them in your appWho this book is for: This book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required.
Salesforce Data Architecture and Management
Learn everything you need to become a successful data architect on the Salesforce platformKey Features: Adopt best practices relating to data governance and learn how to implement themLearn how to work with data in Salesforce while maintaining scalability and security of an instanceGain insights into managing large data volumes in SalesforceBook Description: As Salesforce orgs mature over time, data management and integrations are becoming more challenging than ever. Salesforce Data Architecture and Management follows a hands-on approach to managing data and tracking the performance of your Salesforce org.You'll start by understanding the role and skills required to become a successful data architect. The book focuses on data modeling concepts, how to apply them in Salesforce, and how they relate to objects and fields in Salesforce. You'll learn the intricacies of managing data in Salesforce, starting from understanding why Salesforce has chosen to optimize for read rather than write operations. After developing a solid foundation, you'll explore examples and best practices for managing your data. You'll understand how to manage your master data and discover what the Golden Record is and why it is important for organizations. Next, you'll learn how to align your MDM and CRM strategy with a discussion on Salesforce's Customer 360 and its key components. You'll also cover data governance, its multiple facets, and how GDPR compliance can be achieved with Salesforce. Finally, you'll discover Large Data Volumes (LDVs) and best practices for migrating data using APIs.By the end of this book, you'll be well-versed with data management, data backup, storage, and archiving in Salesforce.What You Will Learn: Understand the Salesforce data architectureExplore various data backup and archival strategiesUnderstand how the Salesforce platform is designed and how it is different from other relational databasesUncover tools that can help in data management that minimize data trust issues in your Salesforce orgFocus on the Salesforce Customer 360 platform, its key components, and how it can help organizations in connecting with customersDiscover how Salesforce can be used for GDPR complianceMeasure and monitor the performance of your Salesforce orgWho this book is for: This book is for aspiring architects, Salesforce admins, and developers. You will also find the book useful if you're preparing for the Salesforce Data Architecture and Management exam. A basic understanding of Salesforce is assumed.
Intelligent Systems and Learning Data Analytics in Online Education
Intelligent Systems and Learning Data Analytics in Online Education provides novel artificial intelligence (AI) and analytics-based methods to improve online teaching and learning. This book addresses key problems such as attrition and lack of engagement in MOOCs and online learning in general. This book explores the state of the art of artificial intelligence, software tools and innovative learning strategies to provide better understanding and solutions to the various challenges of current e-learning in general and MOOC education. In particular, Intelligent Systems and Learning Data Analytics in Online Education shares stimulating theoretical and practical research from leading international experts. This publication provides useful references for educational institutions, industry, academic researchers, professionals, developers, and practitioners to evaluate and apply.
Limitless Analytics with Azure Synapse
Leverage the Azure analytics platform's key analytics services to deliver unmatched intelligence for your dataKey Features: Learn to ingest, prepare, manage, and serve data for immediate business requirementsBring enterprise data warehousing and big data analytics together to gain insights from your dataDevelop end-to-end analytics solutions using Azure SynapseBook Description: Azure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform.The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features.By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks.What You Will Learn: Explore the necessary considerations for data ingestion and orchestration while building analytical pipelinesUnderstand pipelines and activities in Synapse pipelines and use them to construct end-to-end data-driven workflowsQuery data using various coding languages on Azure SynapseFocus on Synapse SQL and Synapse SparkManage and monitor resource utilization and query activity in Azure SynapseConnect Power BI workspaces with Azure Synapse and create or modify reports directly from Synapse StudioCreate and manage IP firewall rules in Azure SynapseWho this book is for: This book is for data engineers, IT professionals, business analysts, data scientists, and database administrators who are looking to get up and running with the Azure Synapse Analytics platform. Basic knowledge of data warehousing will be beneficial to help you understand the concepts covered in this book more effectively.
Machine Learning with BigQuery ML
Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery MLKey Features: Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery MLLeverage SQL syntax to train, evaluate, test, and use ML modelsDiscover how BigQuery works and understand the capabilities of BigQuery ML using examplesBook Description: BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML.The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement.By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.What You Will Learn: Discover how to prepare datasets to build an effective ML modelForecast business KPIs by leveraging various ML models and BigQuery MLBuild and train a recommendation engine to suggest the best products for your customers using BigQuery MLDevelop, train, and share a BigQuery ML model from previous parts with AI Platform NotebooksFind out how to invoke a trained TensorFlow model directly from BigQueryGet to grips with BigQuery ML best practices to maximize your ML performanceWho this book is for: This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.
Expert Data Modeling with Power BI
Manage and work with business data effectively by learning data modeling techniques and leveraging the latest features of Power BIKey Features: Understand data modeling techniques to get the best out of data using Power BIDefine the relationships between data to extract valuable insightsSolve a wide variety of business challenges by building optimal data modelsBook Description: This book is a comprehensive guide to understanding the ins and outs of data modeling and how to create data models using Power BI confidently.You'll learn how to connect data from multiple sources, understand data, define and manage relationships between data, and shape data models to gain deep and detailed insights about your organization.In this book, you'll explore how to use data modeling and navigation techniques to define relationships and create a data model before defining new metrics and performing custom calculations using modeling features. As you advance through the chapters, the book will demonstrate how to create full-fledged data models, enabling you to create efficient data models and simpler DAX code with new data modeling features. With the help of examples, you'll discover how you can solve business challenges by building optimal data models and changing your existing data models to meet evolving business requirements. Finally, you'll learn how to use some new and advanced modeling features to enhance your data models to carry out a wide variety of complex tasks.By the end of this Power BI book, you'll have gained the skills you need to structure data coming from multiple sources in different ways to create optimized data models that support reporting and data analytics.What You Will Learn: Implement virtual tables and time intelligence functionalities in DAX to build a powerful modelIdentify Dimension and Fact tables and implement them in Power Query EditorDeal with advanced data preparation scenarios while building Star SchemaExplore best practices for data preparation and modelingDiscover different hierarchies and their common pitfallsUnderstand complex data models and how to decrease the level of model complexity with different approachesLearn advanced data modeling techniques such as aggregations, incremental refresh, and RLS/OLSWho this book is for: This MS Power BI book is for BI users, data analysts, and analysis developers who want to become well-versed with data modeling techniques to make the most of Power BI. You'll need a solid grasp on basic use cases and functionalities of Power BI and Star Schema functionality before you can dive in.
Towards Autonomous Robotic Systems
The volume LNAI 12228 constitute the refereed proceedings of the 21th Annual Conference "Towards Autonomous Robotics," TAROS 20120, held in Nottingham, UK, in September 2020.*The 30 full papers and 11 short papers presented were carefully reviewed and selected from 63 submissions. The papers present and discuss significant findings and advances in autonomous robotics research and applications. They are organized in the following topical sections: soft and compliant robots; mobile robots; learning, mapping and planning; human-robot interaction; and robotic systems and applications. * The conference was held virtually due to the COVID-19 pandemic.
Ripple-Down Rules
Machine learning algorithms hold out extraordinary promise, but the reality is that their success depends entirely on the suitability of the data available. This book is about Ripple-Down Rules an alternative manual technique for rapidly building AI systems. With a human in the loop, RDR is much better able to deal with the limitations of the data.
Artificial Intelligence and Emerging Technologies in International Relations
Artificial Intelligence and Emerging Technologies in International Relations explores the geopolitics between technology and international relations. Through a focus on war, trade, investment flows, diplomacy, regional integration and development cooperation, this book takes a holistic perspective to examine the origins of technology, analysing its current manifestations in the contemporary world. The authors present the possible future roles of artificial intelligence (AI) and other emerging technologies (including blockchain, 3D printing, 5G connectivity and the Internet of Things) in the context of global arena.This book is essential reading to all who seek to understand the reality of the inequitable distribution of these game-changing technologies that are shaping the world. Research questions as well as some policy options for the developing world are explored and the authors make the case for cooperation by the international community as we enter the fourth industrial revolution.
E. F. Codd and Relational Theory, Revised Edition
E. F. Codd's relational model of data has been described as one of the three greatest inventions of all time (the other two being agriculture and the scientific method), and his receipt of the 1981 ACM Turing Award, the top award in computer science, for inventing it was thoroughly deserved. The papers in which Codd first described his model were staggering in their originality; they had, and continue to have, a huge impact on just about every aspect of the way we do business in the world today. And yet few people, even in the professional database community, are truly familiar with those papers. This book-a thorough overhaul and rewrite of an earlier book by the same name-is an attempt to remedy this sorry state of affairs. In it, well known author C. J. Date provides a detailed examination of all of Codd's major database publications, explaining the nature of his contribution in depth, and in particular highlighting not only the many things he got right but also some of the things he got wrong. Database theory and practice have evolved considerably since Codd first defined his relational model, back in 1969. This book draws on decades of experience to present the most up to date treatment of the material possible. Anyone with a professional interest in databases can benefit fr
Datadog Cloud Monitoring Quick Start Guide
A comprehensive guide to rolling out Datadog to monitor infrastructure and applications running in both cloud and datacenter environmentsKey Features: Learn Datadog to proactively monitor your infrastructure and cloud servicesUse Datadog as a platform for aggregating monitoring efforts in your organizationLeverage Datadog's alerting service to implement on-call and site reliability engineering (SRE) processesBook Description: Datadog is an essential cloud monitoring and operational analytics tool which enables the monitoring of servers, virtual machines, containers, databases, third-party tools, and application services. IT and DevOps teams can easily leverage Datadog to monitor infrastructure and cloud services, and this book will show you how.The book starts by describing basic monitoring concepts and types of monitoring that are rolled out in a large-scale IT production engineering environment. Moving on, the book covers how standard monitoring features are implemented on the Datadog platform and how they can be rolled out in a real-world production environment. As you advance, you'll discover how Datadog is integrated with popular software components that are used to build cloud platforms. The book also provides details on how to use monitoring standards such as Java Management Extensions (JMX) and StatsD to extend the Datadog platform. Finally, you'll get to grips with monitoring fundamentals, learn how monitoring can be rolled out using Datadog proactively, and find out how to extend and customize the Datadog platform.By the end of this Datadog book, you will have gained the skills needed to monitor your cloud infrastructure and the software applications running on it using Datadog.What You Will Learn: Understand monitoring fundamentals, including metrics, monitors, alerts, and thresholdsImplement core monitoring requirements using Datadog featuresExplore Datadog's integration with cloud platforms and toolsExtend Datadog using custom scripting and standards such as JMX and StatsDDiscover how proactive monitoring can be rolled out using various Datadog featuresUnderstand how Datadog can be used to monitor microservices in both Docker and Kubernetes environmentsGet to grips with advanced Datadog features such as APM and Security MonitoringWho this book is for: This book is for DevOps engineers, site reliability engineers (SREs), IT Production engineers, software developers and architects, cloud engineers, system administrators, and anyone looking to monitor and visualize their infrastructure and applications with Datadog. Basic working knowledge of cloud and infrastructure is useful. Working experience of Linux distribution and some scripting knowledge is required to fully take advantage of the material provided in the book.
Combinatorial Algorithms
This book constitutes the proceedings of the 32nd International Workshop on Combinatorial Algorithms which was planned to take place in Ottawa, ON, Canada, in July 2021. Due to the COVID-19 pandemic the conference changed to a virtual format.The 38 full papers included in this book together with 2 invited talks were carefully reviewed and selected from 107 submissions. They focus on algorithms design for the myriad of combinatorial problems that underlie computer applications in science, engineering and business.Chapter "Minimum Eccentricity Shortest Path Problem with Respect to Structural Parameters" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Interactive Dashboards and Data Apps with Plotly and Dash
Build web-based, mobile-friendly analytic apps and interactive dashboards with PythonKey FeaturesDevelop data apps and dashboards without any knowledge of JavaScriptMap different types of data such as integers, floats, and dates to bar charts, scatter plots, and moreCreate controls and visual elements with multiple inputs and outputs and add functionality to the app as per your requirementsBook DescriptionPlotly's Dash framework is a life-saver for Python developers who want to develop complete data apps and interactive dashboards without JavaScript, but you'll need to have the right guide to make sure you're getting the most of it. With the help of this book, you'll be able to explore the functionalities of Dash for visualizing data in different ways.Interactive Dashboards and Data Apps with Plotly and Dash will first give you an overview of the Dash ecosystem, its main packages, and the third-party packages crucial for structuring and building different parts of your apps. You'll learn how to create a basic Dash app and add different features to it.Next, you'll integrate controls such as dropdowns, checkboxes, sliders, date pickers, and more in the app and then link them to charts and other outputs. Depending on the data you are visualizing, you'll also add several types of charts, including scatter plots, line plots, bar charts, histograms, and maps, as well as explore the options available for customizing them.By the end of this book, you'll have developed the skills you need to create and deploy an interactive dashboard, handle complexities and code refactoring, and understand the process of improving your application.What you will learnFind out how to run a fully interactive and easy-to-use appConvert your charts to various formats including images and HTML filesUse Plotly Express and the grammar of graphics for easily mapping data to various visual attributesCreate different chart types, such as bar charts, scatter plots, histograms, maps, and moreExpand your app by creating dynamic pages that generate content based on URLsImplement new callbacks to manage charts based on URLs and vice versaWho this book is forThis Plotly Dash book is for data professionals and data analysts who want to gain a better understanding of their data with the help of different visualizations and dashboards - and without having to use JS. Basic knowledge of the Python programming language and HTML will help you to grasp the concepts covered in this book more effectively, but it's not a prerequisite.Table of ContentsOverview of the Dash EcosystemExploring the Structure of a Dash AppWorking with Plotly's Figure ObjectsData Manipulation and Preparation - Paving the Way to Plotly ExpressInteractively Comparing Values with Bar Charts and Dropdown MenusExploring Variables with Scatter Plots and Filtering Subsets with SlidersExploring Map Plots and Enriching Your Dashboards with MarkdownCalculating Data Frequency and Building Interactive TablesLetting Your Data Speak for Itself with Machine LearningTurbo-charge Your Apps with Advanced CallbacksURLs and Multi-Page AppsDeploying Your AppNext Steps
