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Название: Linkerd: Up and Running: A Guide to Operationalizing a Kubernetes-native Service Mesh
Автор: Jason Morgan and Flynn
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 150
Язык: английский
Формат: epub (true)
Размер: 10.1 MB

With the massive adoption of microservices, operators and developers face far more complexity in their applications today. Service meshes can help you manage this problem by providing a unified control plane to secure, manage, and monitor your entire network. This practical guide shows you how the Linkerd service mesh enables cloud-native developers—including platform and site reliability engineers—to solve the thorny issue of running distributed applications in Kubernetes. Jason Morgan and Flynn draw on their years of experience at Buoyant—the creators of Linkerd—to demonstrate how this service mesh can help ensure that your applications are secure, observable, and reliable. You'll understand why Linkerd, the original service mesh, can still claim the lowest time to value of any mesh option available today. When the Linkerd2 rewrite started, the Rust programming language had been gaining attention for its memory safety, which enables developers to write code that avoids many of the memory management vulnerabilities inherent to C and C++, while still compiling to native code for high performance. This book is meant to help anyone who thinks it’s easier to get things done when creating, running, or debugging microservices applications, and is looking to Linkerd to help with that. You’ll need some basic knowledge of Kubernetes, the overall concept of running things in containers, and the Unix command line to get the most out of this book. Some familiarity with Prometheus, Helm, Jaeger, etc. will also be helpful, but isn’t really critical.

Название: Software Engineering for Data Scientists: From Notebooks to Scalable Systems (Final)
Автор: Catherine Nelson
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 400
Язык: английский
Формат: pdf, epub (true)
Размер: 11.2 MB

Data Science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to Data Science. All the code examples in this book are written in Python, and many of the chapters describe Python-specific tools. In recent years, Python has become the most popular programming language for Data Science. Python has an extremely solid set of open source libraries for data science, with good backing and a healthy community of maintainers. Large trend-setting companies have chosen Python for their main ML frameworks, including TensorFlow (Google) and PyTorch (Meta). Because of this, Python appears to be especially popular among data scientists working on production Machine Learning code, where good coding skills are particularly important. In my experience, the Python community has been friendly and welcoming, with many excellent events that have helped me improve my skills. It’s my preferred programming language, so it was an easy choice for this book. This book is aimed at data scientists, but people working in closely related fields such as data analysts, Machine Learning (ML) engineers, and data engineers will also find it useful.

Название: Программировать в 1С за 11 шагов. 3-изд
Автор: Ильяс Низамутдинов
Издательство: Ижевск
Год: 2022
Формат: PDF
Страниц: 778
Размер: 22 Mb
Язык: Русский

Программисты других специальностей часто сталкиваются с задачами по разработке в 1С, поэтому, изучив язык программирования в 1С, специалист улучшит свою квалификацию и увеличит собственный доход.

Название: Deep Learning in Internet of Things for Next Generation Healthcare
Автор: Lavanya Sharma, Pradeep Kumar Garg
Издательство: CRC Press
Год: 2024
Страниц: 311
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

This book presents the latest developments in Deep Learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with Deep Learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of Deep Learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with Deep Learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, and genomes. Convolutional neural networks (CNNs), in particular, are excellent at extracting hierarchical characteristics from images using Deep Learning models. These models learn to recognize edges, textures, forms, and even intricate patterns inside images in the context of human vision. CNNs may automatically identify pertinent features for IoT applications from unprocessed visual data without the need for explicit feature engineering. The classifcation and recognition of objects is one of the most important uses of Deep Learning and human vision in IoT. Deep Learning models can be taught to identify and categorize objects in frames of pictures or videos. Security (intruder detection), retail (product identifcation), healthcare (medical image analysis), and other felds all make use of these capabilities. Postgraduate students and researchers in the departments of Computer Science, working in the areas of the IoT, Deep Learning, Machine Learning, image processing, Big Data, cloud computing, and remote sensing will find this book useful.

Название: Building Micro-Frontends: Distributed Systems for the Frontend, 2nd Edition (Early Release)
Автор: Luca Mezzalira
Издательство: O’Reilly Media, Inc.
Год: 2024-04-15
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

What's the answer to today's increasingly complex applications? Micro-frontends. Inspired by the microservices model, this approach lets you break interfaces into separate features managed by different teams of developers. In this updated second edition, software architects, tech leads, and software developers will learn how to design, build, and deploy independent micro-frontends that compose unique frontend systems. Author Luca Mezzalira, principal serverless specialist solutions architect at AWS, shows you how micro-frontends enable agility within an organization, decentralize decision-making, and optimize for fast flow. This gives your organization technical flexibility and allows you to hire and retain a broad spectrum of talent. Micro-frontends also support distributed or colocated teams more efficiently. Pick up this book and learn how to get started with this technological breakthrough right away. Nowadays microservices architecture is a well-known, established and popular pattern used by many organizations across the world. Microservices split a unique codebase into smaller parts, each of them with a subset of functionalities compared to a monolith. This business logic is embraced by developers because the problem solved by a microservice is simpler than looking at thousands of lines of code. Moreover a developer can maintain a clear picture of the code base and related functionality implemented, considering the cognitive load is by far less than working on a monolithic system.

Название: Data Storytelling with Generative AI: using Python and Altair (MEAP v5)
Автор: Angelica Lo Duca
Издательство: Manning Publications
Год: 2024
Страниц: 425
Язык: английский
Формат: pdf, epub
Размер: 46.5 MB

Great data presentations tell a story. Learn how to organize, visualize, and present data using Python, generative AI, and the cutting-edge Altair data analysis toolkit. Data Storytelling with Python Altair and Generative AI teaches you how to turn raw data into effective, insightful data stories. You’ll learn exactly what goes into an effective data story, then combine your Python data skills with the Altair library and AI tools to rapidly create amazing visualizations. Your bosses and decision-makers will love your new presentations—and you’ll love how quick Generative AI makes the whole process! Python is the second ingredient of this book. As a data journalism professor, I have experimented with many Python libraries for data visualization, such as Matplotlib, Plotly, and Seaborn. However, at the end of my experiments, I realized that the simplest library for data visualization is Altair. Unlike other libraries, Altair is declarative, thus enabling you only to focus on the output of your data visualization. Thus, this book will focus on Altair to build data visualization. Although you can find many materials on the web about Altair, they focus only on how to build raw charts. In this book, you’ll learn how to build data stories with Altair and not simply raw charts. Last but not least, you’ll learn how to use Generative AI tools for data storytelling. This book won’t focus on Generative AI concepts and theory. Instead, you will learn how to apply Generative AI tools, including GitHub Copilot, ChatGPT, and DALL-E, to data storytelling.

Название: Data-Centric Artificial Intelligence for Multidisciplinary Applications
Автор: Parikshit N. Mahalle, Namrata N. Wasatkar, Gitanjali R. Shinde
Издательство: CRC Press
Год: 2024
Страниц: 309
Язык: английский
Формат: pdf (true)
Размер: 19.4 MB

This book explores the need for a Data-Centric Artificial Intelligence (AI) approach and its application in the multidisciplinary domain, compared to a model‑centric approach. It examines the methodologies for data‑centric approaches, the use of data‑centric approaches in different domains, the need for edge AI and how it differs from cloud‑based AI. It discusses the new category of AI technology, "data‑centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding Machine Learning and Big Data analytics tools, data‑centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods. Data-Centric Artificial Intelligence (AI) denotes an approach within AI and Machine Learning (ML) that places significant emphasis on the pivotal role of meticulously curated, high‑quality data in the development and implementation of AI models and systems. Under this paradigm, data assumes the bedrock upon which AI algorithms are constructed and honed, and its effective handling, preprocessing, and analysis stand as pivotal factors for achieving precise and dependable AI outcomes.

Название: AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications
Автор: Sita Rani, Pankaj Bhambri, Sachin Kumar
Издательство: CRC Press
Год: 2024
Страниц: 339
Язык: английский
Формат: pdf (true)
Размер: 22.0 MB

AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications introduces the role of Digital Twin in Smart Manufacturing and focuses on the Digital Twin framework throughout. It provides a summary of the various AI applications in the Industry 4.0 environment and emphasizes the role of advanced computational and communication technologies. The book offers demonstrative examples of AI-Driven Digital Twin in various application domains and includes AI techniques used to analyze the environmental impact of industrial operations along with examples. The book reviews the major challenges in the deployment of AI-Driven Digital Twin in the Industry 4.0 ecosystem and presents an understanding of how AI is used in the designing of Digital Twin for various applications. This literary work delves into the revolutionary convergence of Artificial Intelligence (AI), Digital Twins, and Industry 4.0, providing a detailed examination of the theoretical underpinnings and practical implementations within this swiftly advancing domain. Artificial Intelligence (AI), Machine Learning, Deep Learning, Internet of Things (IoT), Cloud Computing, Digital Twin, and the most recent connectivity services are the most prominent of the era’s cutting-edge technologies that are assisting users intelligently and predicting their behavior, as well as providing the connectivity and convenience of data to satisfy the majority of their needs. The target audience of this book encompasses a broad spectrum, ranging from scholars and learners to practitioners and experts in the fields of research, education, engineering, and industry. The book aims to provide comprehensive insights into the interplay between AI, digital twins, and the Fourth Industrial Revolution, also known as Industry 4.0.

Название: Handbook of Digital Twins
Автор: Zhihan Lyu
Издательство: CRC Press
Год: 2024
Страниц: 923
Язык: английский
Формат: pdf (true)
Размер: 13.5 MB

Over the last two decades, Digital Twins (DTs) have become the intelligent representation of future development in industrial production and daily life. Consisting of over 50 chapters by more than 100 contributors, this comprehensive handbook explains the concept, architecture, design specification and application scenarios of DTs. As a virtual model of a process, product or service to pair the virtual and physical worlds, DTs allow data analysis and system monitoring by using simulations. The fast-growing technology has been widely studied and developed in recent years. Featured with centralization, integrity and dynamics, it is cost-effective to drive innovation and performance. Many fields saw the adaptation and implementation across industrial production, healthcare, smart city, transportation and logistics. World-famous enterprises such as Siemens, Tesla, ANSYS and General Electric have built smart factories and pioneered digital production, heading towards Industry 4.0. The Cognitive Digital Twins (CDT) for manufacturing systems was proposed based on advances in cognitive science, Artificial Intelligence (AI) technologies, and Machine Learning techniques. According to the fundamental aspects of cognition, they define CDT as a DT with additional cognitive capabilities, including perception, attention, memory, reasoning, problem‑solving and learning. Recall that a Digital Twin (DT) is a virtual copy of a physical asset that promptly reproduces and alters the state and behavior of the asset. DT intensely employs Industry 4.0 data‑driven Artificial Intelligence technologies and simulations for adaptive monitoring, forecasting, optimization, and controlling asset performance.

Название: Kafka Streams in Action: Event-driven applications and microservices, 2nd Edition (Final Release)
Автор: Bill Bejeck
Издательство: Manning Publications
Год: 2024
Страниц: 506
Язык: английский
Формат: pdf (true)
Размер: 16.5 MB

Everything you need to implement stream processing on Apache Kafka using Kafka Streams and the kqsIDB event streaming database. Kafka Streams in Action, Second Edition guides you through setting up and maintaining your streaming processing with Kafka. Inside, you’ll find comprehensive coverage of not only Kafka Streams, but the entire toolbox you’ll need for effective streaming—from the components of the Kafka ecosystem, to Producer and Consumer clients, Connect, and Schema Registry. Kafka Streams in Action, Second Edition teaches you how to create event streaming applications on the amazing Apache Kafka platform. This thoroughly revised new edition now covers a wider range of streaming architectures and includes data integration with Kafka Connect. As you go, you’ll explore real-world examples that introduce components and brokers, schema management, and the other essentials. Along the way, you’ll pick up practical techniques for blending Kafka with Spring, low-level control of processors and state stores, storing event data with ksqlDB, and testing streaming applications. For Java developers. No knowledge of Kafka or streaming applications required.