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Distributed Computing with Python
by Francesco PierfedericiHarness the power of multiple computers using Python through this fast-paced informative guide About This Book * You'll learn to write data processing programs in Python that are highly available, reliable, and fault tolerant * Make use of Amazon Web Services along with Python to establish a powerful remote computation system * Train Python to handle data-intensive and resource hungry applications Who This Book Is For This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks. What You Will Learn * Get an introduction to parallel and distributed computing * See synchronous and asynchronous programming * Explore parallelism in Python * Distributed application with Celery * Python in the Cloud * Python on an HPC cluster * Test and debug distributed applications In Detail CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications. This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more. Style and Approach This example based, step-by-step guide will show you how to make the best of your hardware configuration using Python for distributing applications.
Distributed Consensus with Visual Perception in Multi-Robot Systems
by Eduardo Montijano Carlos SagüésThis monograph introduces novel responses to the different problems that arise when multiple robots need to execute a task in cooperation, each robot in the team having a monocular camera as its primary input sensor. Its central proposition is that a consistent perception of the world is crucial for the good development of any multi-robot application. The text focuses on the high-level problem of cooperative perception by a multi-robot system: the idea that, depending on what each robot sees and its current situation, it will need to communicate these things to its fellows whenever possible to share what it has found and keep updated by them in its turn. However, in any realistic scenario, distributed solutions to this problem are not trivial and need to be addressed from as many angles as possible. Distributed Consensus with Visual Perception in Multi-Robot Systems covers a variety of related topics such as: * distributed consensus algorithms; * data association and robustness problems; * convergence speed; and * cooperative mapping. The book first puts forward algorithmic solutions to these problems and then supports them with empirical validations working with real images. It provides the reader with a deeper understanding of the problems associated to the perception of the world by a team of cooperating robots with onboard cameras. Academic researchers and graduate students working with multi-robot systems, or investigating problems of distributed control or computer vision and cooperative perception will find this book of material assistance with their studies.
Distributed Context-Aware Systems
by Paulo Ferreira Pedro AlvesContext-aware systems aim to deliver a rich user experience by taking into account the current user context (location, time, activity, etc. ), possibly captured without his intervention. For example, cell phones are now able to continuously update a user's location while, at the same time, users execute an increasing amount of activities online, where their actions may be easily captured (e. g. login in a web application) without user consent. In the last decade, this topic has seen numerous developments that demonstrate its relevance and usefulness. The trend was accelerated with the widespread availability of powerful mobile devices (e. g. smartphones) that include a myriad of sensors which enable applications to capture the user context. However, there are several challenges that must be addressed; we focus on scalability (large number of context aware messages) and privacy (personal data that may be propagated). This book is organized in five chapters starting with an introduction to the theme raising the most important challenges. Then, chapter two presents several important definitions (establishing a common ground for the following chapters) and taxonomy. These are important to chapter three which describes some of the most relevant distributed context-aware systems that can be classified according to the taxonomy. Privacy is addressed in chapter four and chapter five presents some important conclusions. The audience for this book is wide; researchers, students and professionals interested in the areas addressed will find the most relevant information regarding scalability and privacy in distributed context-aware systems.
Distributed Data Fusion for Network-Centric Operations
by David L. Hall Chee-Yee Chong James Llinas Martin Liggins IIWith the recent proliferation of service-oriented architectures (SOA), cloud computing technologies, and distributed-interconnected systems, distributed fusion is taking on a larger role in a variety of applications—from environmental monitoring and crisis management to intelligent buildings and defense. Drawing on the work of leading experts around the world, Distributed Data Fusion for Network-Centric Operations examines the state of the art of data fusion in a distributed sensing, communications, and computing environment. Get Insight into Designing and Implementing Data Fusion in a Distributed Network Addressing the entirety of information fusion, the contributors cover everything from signal and image processing, through estimation, to situation awareness. In particular, the work offers a timely look at the issues and solutions involving fusion within a distributed network enterprise. These include critical design problems, such as how to maintain a pedigree of agents or nodes that receive information, provide their contribution to the dataset, and pass to other network components. The book also tackles dynamic data sharing within a network-centric enterprise, distributed fusion effects on state estimation, graph-theoretic methods to optimize fusion performance, human engineering factors, and computer ontologies for higher levels of situation assessment. A comprehensive introduction to this emerging field and its challenges, the book explores how data fusion can be used within grid, distributed, and cloud computing architectures. Bringing together both theoretical and applied research perspectives, this is a valuable reference for fusion researchers and practitioners. It offers guidance and insight for those working on the complex issues of designing and implementing distributed, decentralized information fusion.
Distributed Data Systems with Azure Databricks: Create, deploy, and manage enterprise data pipelines
by Alan Bernardo PalacioQuickly build and deploy massive data pipelines and improve productivity using Azure DatabricksKey FeaturesGet to grips with the distributed training and deployment of machine learning and deep learning modelsLearn how ETLs are integrated with Azure Data Factory and Delta LakeExplore deep learning and machine learning models in a distributed computing infrastructureBook DescriptionMicrosoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you'll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you'll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you'll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you'll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.What you will learnCreate ETLs for big data in Azure DatabricksTrain, manage, and deploy machine learning and deep learning modelsIntegrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creationDiscover how to use Horovod for distributed deep learningFind out how to use Delta Engine to query and process data from Delta LakeUnderstand how to use Data Factory in combination with DatabricksUse Structured Streaming in a production-like environmentWho this book is forThis book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.
Distributed Database Management Systems: A Practical Approach
by Saeed K. Rahimi Frank S. HaugThis book addresses issues related to managing data across a distributed database system. It is unique because it covers traditional database theory and current research, explaining the difficulties in providing a unified user interface and global data dictionary. The book gives implementers guidance on hiding discrepancies across systems and creating the illusion of a single repository for users. It also includes three sample frameworks—implemented using J2SE with JMS, J2EE, and Microsoft .Net—that readers can use to learn how to implement a distributed database management system. IT and development groups and computer sciences/software engineering graduates will find this guide invaluable.
Distributed Denial of Service Attack and Defense
by Shui YuThis brief provides readers a complete and self-contained resource for information about DDoS attacks and how to defend against them. It presents the latest developments in this increasingly crucial field along with background context and survey material. The book also supplies an overview of DDoS attack issues, DDoS attack detection methods, DDoS attack source traceback, and details on how hackers organize DDoS attacks. The author concludes with future directions of the field, including the impact of DDoS attacks on cloud computing and cloud technology. The concise yet comprehensive nature of this brief makes it an ideal reference for researchers and professionals studying DDoS attacks. It is also a useful resource for graduate students interested in cyberterrorism and networking.
Distributed Denial of Service Attacks: Real-world Detection and Mitigation
by İlker Özçelik Richard BrooksDistributed Denial of Service (DDoS) attacks have become more destructive, wide-spread and harder to control over time. This book allows students to understand how these attacks are constructed, the security flaws they leverage, why they are effective, how they can be detected, and how they can be mitigated. Students use software defined networking (SDN) technology to created and execute controlled DDoS experiments. They learn how to deploy networks, analyze network performance, and create resilient systems. This book is used for graduate level computer engineering instruction at Clemson University. It augments the traditional graduate computing curricula by integrating: Internet deployment, network security, ethics, contemporary social issues, and engineering principles into a laboratory based course of instruction. Unique features of this book include: A history of DDoS attacks that includes attacker motivations Discussion of cyber-war, censorship, and Internet black-outs SDN based DDoS laboratory assignments Up-to-date review of current DDoS attack techniques and tools Review of the current laws that globally relate to DDoS Abuse of DNS, NTP, BGP and other parts of the global Internet infrastructure to attack networks Mathematics of Internet traffic measurement Game theory for DDoS resilience Construction of content distribution systems that absorb DDoS attacks This book assumes familiarity with computing, Internet design, appropriate background in mathematics, and some programming skills. It provides analysis and reference material for networking engineers and researchers. By increasing student knowledge in security, and networking; it adds breadth and depth to advanced computing curricula.
Distributed Denial of Service (DDoS) Attacks: Classification, Attacks, Challenges and Countermeasures
by Brij B. Gupta Amrita DahiyaThe complexity and severity of the Distributed Denial of Service (DDoS) attacks are increasing day-by-day. The Internet has a highly inconsistent structure in terms of resource distribution. Numerous technical solutions are available, but those involving economic aspects have not been given much consideration. The book, DDoS Attacks – Classification, Attacks, Challenges, and Countermeasures, provides an overview of both types of defensive solutions proposed so far, exploring different dimensions that would mitigate the DDoS effectively and show the implications associated with them. Features: Covers topics that describe taxonomies of the DDoS attacks in detail, recent trends and classification of defensive mechanisms on the basis of deployment location, the types of defensive action, and the solutions offering economic incentives. Introduces chapters discussing the various types of DDoS attack associated with different layers of security, an attacker’s motivations, and the importance of incentives and liabilities in any defensive solution. Illustrates the role of fair resource-allocation schemes, separate payment mechanisms for attackers and legitimate users, negotiation models on cost and types of resources, and risk assessments and transfer mechanisms. DDoS Attacks – Classification, Attacks, Challenges, and Countermeasures is designed for the readers who have an interest in the cybersecurity domain, including students and researchers who are exploring different dimensions associated with the DDoS attack, developers and security professionals who are focusing on developing defensive schemes and applications for detecting or mitigating the DDoS attacks, and faculty members across different universities.
Distributed, Embedded and Real-time Java Systems
by M. Teresa Higuera-Toledano Andy J. WellingsResearch on real-time Java technology has been prolific over the past decade, leading to a large number of corresponding hardware and software solutions, and frameworks for distributed and embedded real-time Java systems. This book is aimed primarily at researchers in real-time embedded systems, particularly those who wish to understand the current state of the art in using Java in this domain. Much of the work in real-time distributed, embedded and real-time Java has focused on the Real-time Specification for Java (RTSJ) as the underlying base technology, and consequently many of the Chapters in this book address issues with, or solve problems using, this framework. Describes innovative techniques in: scheduling, memory management, quality of service and communication systems supporting real-time Java applications;Includes coverage of multiprocessor embedded systems and parallel programming;Discusses state-of-the-art resource management for embedded systems, including Java's real-time garbage collection and parallel collectors;Considers hardware support for the execution of Java programs including how programs can interact with functional accelerators;Includes coverage of Safety Critical Java for development of safety critical embedded systems.
Distributed Embedded Controller Development with Petri Nets
by Filipe Moutinho Luís GomesThis book describes a model-based development approach for globally-asynchronous locally-synchronous distributed embedded controllers. This approach uses Petri nets as modeling formalism to create platform and network independent models supporting the use of design automation tools. To support this development approach, the Petri nets class in use is extended with time-domains and asynchronous-channels. The authors' approach uses models not only providing a better understanding of the distributed controller and improving the communication among the stakeholders, but also to be ready to support the entire lifecycle, including the simulation, the verification (using model-checking tools), the implementation (relying on automatic code generators), and the deployment of the distributed controller into specific platforms. Uses a graphical and intuitive modeling formalism supported by design automation tools; Enables verification, ensuring that the distributed controller was correctly specified; Provides flexibility in the implementation and maintenance phases to achieve desired constraints (high performance, low power consumption, reduced costs), enabling porting to different platforms using different communication nodes, without changing the underlying behavioral model.
Distributed Embedded Smart Cameras
by Christophe Bobda Senem VelipasalarThis publication addresses distributed embedded smart cameras -cameras that perform on board analysis and collaborate with other cameras. This book provides the material required to better understand the architectural design challenges of embedded smart camera systems, the hardware/software ecosystem, the design approach for and applications of distributed smart cameras together with the state-of-the-art algorithms. The authors concentrate on the architecture, hardware/software design, realization of smart camera networks from applications to architectures, in particular in the embedded and mobile domains.
Distributed Fusion Estimation for Sensor Networks with Communication Constraints
by Wen-An Zhang Bo Chen Haiyu Song Li YuThisbook systematically presents energy-efficient robust fusion estimation methodsto achieve thorough and comprehensive results in the context of network-basedfusion estimation. It summarizes recent findings on fusion estimation withcommunication constraints; several novel energy-efficient and robust designmethods for dealing with energy constraints and network-induced uncertaintiesare presented, such as delays, packet losses, and asynchronous information. . . All the results are presented as algorithms, which are convenient for practicalapplications.
Distributed Game Development: Harnessing Global Talent to Create Winning Games
by Tim FieldsTake control of your global game development team and make successful AAA game titles using the 'Distributed Development' model. Game industry veteran Tim Fields teaches you how to evaluate game deals, how to staff teams for highly distributed game development, and how to maintain challenging relationships in order to get great games to market. This book is filled with interviews with a broad spectrum of industry experts from top game publishers and business owners in the US and UK. A supplementary web site provides interviews from the book, a forum where developers and publishers can connect, and additional tips and tricks. Topics include:
Distributed Geolibraries: Spatial Information Resources Summary of a Workshop
by National Research CouncilPresents the findings of the Workshop on Distributed Geolibraries: Spatial Information Resources, convened by the Mapping Science Committee of the National Research Council in June 1998
Distributed Graph Algorithms for Computer Networks
by K. ErciyesThis book presents a comprehensive review of key distributed graph algorithms for computer network applications, with a particular emphasis on practical implementation. Topics and features: introduces a range of fundamental graph algorithms, covering spanning trees, graph traversal algorithms, routing algorithms, and self-stabilization; reviews graph-theoretical distributed approximation algorithms with applications in ad hoc wireless networks; describes in detail the implementation of each algorithm, with extensive use of supporting examples, and discusses their concrete network applications; examines key graph-theoretical algorithm concepts, such as dominating sets, and parameters for mobility and energy levels of nodes in wireless ad hoc networks, and provides a contemporary survey of each topic; presents a simple simulator, developed to run distributed algorithms; provides practical exercises at the end of each chapter.
Distributed Hash Table
by Hao Zhang Yonggang Wen Haiyong Xie Nenghai YuThis SpringerBrief summarizes the development of Distributed Hash Table in both academic and industrial fields. It covers the main theory, platforms and applications of this key part in distributed systems and applications, especially in large-scale distributed environments. The authors teach the principles of several popular DHT platforms that can solve practical problems such as load balance, multiple replicas, consistency and latency. They also propose DHT-based applications including multicast, anycast, distributed file systems, search, storage, content delivery network, file sharing and communication. These platforms and applications are used in both academic and commercials fields, making Distributed Hash Table a valuable resource for researchers and industry professionals.
Distributed Heterogeneous Multi Sensor Task Allocation Systems (Automation, Collaboration, & E-Services #7)
by Itshak Tkach Yael EdanToday’s real-world problems and applications in sensory systems and target detection require efficient, comprehensive and fault-tolerant multi-sensor allocation. This book presents the theory and applications of novel methods developed for such sophisticated systems. It discusses the advances in multi-agent systems and AI along with collaborative control theory and tools. Further, it examines the formulation and development of an allocation framework for heterogeneous multi-sensor systems for various real-world problems that require sensors with different performances to allocate multiple tasks, with unknown a priori priorities that arrive at unknown locations at unknown time. It demonstrates how to decide which sensor to allocate to which tasks when and where. Lastly, it explains the reliability and availability issues of task allocation systems, and includes methods for their optimization.The presented methods are explained, measured, and evaluated by extensive simulations, and the results of these simulations are presented in this book. This book is an ideal resource for academics, researchers and graduate students as well as engineers and professionals and is relevant for various applications such as sensor network design, multi-agent systems, task allocation, target detection, and team formation.
Distributed Large-Scale Dimensional Metrology
by Domenico Maisano Fiorenzo Franceschini Luca Mastrogiacomo Barbara Pralio Maurizio GalettoThe field of large-scale dimensional metrology (LSM) deals with objects that have linear dimensions ranging from tens to hundreds of meters. It has recently attracted a great deal of interest in many areas of production, including the automotive, railway, and shipbuilding sectors. Distributed Large-Scale Dimensional Metrology introduces a new paradigm in this field that reverses the classical metrological approach: measuring systems that are portable and can be easily moved around the location of the measured object, which is preferable to moving the object itself. Distributed Large-Scale Dimensional Metrology combines the concepts of distributed systems and large scale metrology at the application level. It focuses on the latest insights and challenges of this new generation of systems from the perspective of the designers and developers. The main topics are: coverage of measuring area,sensors calibration,on-line diagnostics,probe management, andanalysis of metrological performance.The general descriptions of each topic are further enriched by specific examples concerning the use of commercially available systems or the development of new prototypes. This will be particularly useful for professional practitioners such as quality engineers, manufacturing and development engineers, and procurement specialists, but Distributed Large-Scale Dimensional Metrology also has a wealth of information for interested academics.
Distributed Learning: Social and Cultural Approaches to Practice
by Mary R. Lea Kathy NicollAt a time of increasing globalisation, the concept of open and distance learning is being constantly redefined. New technologies have opened up new ways of understanding and participating in Learning. Distributed Learning offers a collection of perspectives from a social and cultural practice-based viewpoint, with contributions from leading international authors in the field. Key issues in this comprehensive text are:*the challenges of ICT to traditional teaching and learning practices*the value and relevance of 'activity theory' and 'communities of practice' in educational institutions and the workplace*perspectives on the relationship between globalisation and distributed learning, and the breakdown of distinctions between global and local contexts*issues of identity and community in designing courses for the virtual student*language and literacies in distributed learning contextsThis book provides useful introductory reading, building a sound theoretical framework for practitioners interested in how distributed learning is shaping post-compulsory education.
Distributed Ledger Technology: 7th International Symposium, SDLT 2023, Brisbane, QLD, Australia, November 30 – December 1, 2023, Revised Selected Papers (Communications in Computer and Information Science #1975)
by Naipeng Dong Babu Pillai Guangdong Bai Mark UttingThis book constitutes the proceedings of the 7th International Symposium, SDLT 2023, held in Brisbane, QLD, Australia, during November 30 – December 1, 2023.The 8 full papers and the short paper included in this volume were carefully reviewed and selected from 32 submissions. The volume focuses on current systems and new solutions to create a scientific background for a solid development of innovative Distributed Ledger Technology application.
Distributed Ledgers: Design and Regulation of Financial Infrastructure and Payment Systems
by Robert M. TownsendAn economic analysis of what distributed ledgers can do, examining key components and discussing applications in both developed and emerging market economies.Distributed ledger technology (DLT) has the potential to transform economic organization and financial structure. In this book, Robert Townsend steps back from the hype and controversy surrounding DLT (and the related, but not synonymous, innovations of blockchain and Bitcoin) to offer an economic analysis of what distributed ledgers can do. Townsend examines the key components of distributed ledgers, discussing, evaluating, and illustrating each in the context of historical and contemporary economics, and reviewing featured applications in both developed economies and emerging-market countries.
A Distributed Linear Programming Models in a Smart Grid (Power Electronics and Power Systems)
by Prakash Ranganathan Kendall E. NygardThis book showcases the strengths of Linear Programming models for Cyber Physical Systems (CPS), such as the Smart Grids. Cyber-Physical Systems (CPS) consist of computational components interconnected by computer networks that monitor and control switched physical entities interconnected by physical infrastructures. A fundamental challenge in the design and analysis of CPS is the lack of understanding in formulating constraints for complex networks. We address this challenge by employing collection of Linear programming solvers that models the constraints of sub-systems and micro grids in a distributed fashion. The book can be treated as a useful resource to adaptively schedule resource transfers between nodes in a smart power grid. In addition, the feasibility conditions and constraints outlined in the book will enable in reaching optimal values that can help maintain the stability of both the computer network and the physical systems. It details the collection of optimization methods that are reliable for electric-utilities to use for resource scheduling, and optimizing their existing systems or sub-systems. The authors answer to key questions on ways to optimally allocate resources during outages, and contingency cases (e. g. , line failures, and/or circuit breaker failures), how to design de-centralized methods for carrying out tasks using decomposition models; and how to quantify un-certainty and make decisions in the event of grid failures.
Distributed Machine Learning and Gradient Optimization (Big Data Management)
by Jiawei Jiang Bin Cui Ce ZhangThis book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.
Distributed Machine Learning Patterns
by Yuan TangPractical patterns for scaling machine learning from your laptop to a distributed cluster.Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges of scaling machine learning systems. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Build ML pipelines with data ingestion, distributed training, model serving, and more Automate ML tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows Make trade-offs between different patterns and approaches Manage and monitor machine learning workloads at scale Inside Distributed Machine Learning Patterns you&’ll learn to apply established distributed systems patterns to machine learning projects—plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. About the technology Deploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kubeflow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster. About the book Distributed Machine Learning Patterns provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you&’ll learn patterns for distributed model training, managing unexpected failures, and dynamic model serving. You&’ll appreciate the practical examples that accompany each pattern along with a full-scale project that implements distributed model training and inference with autoscaling on Kubernetes. What's inside Data ingestion, distributed training, model serving, and more Automating Kubernetes and TensorFlow with Kubeflow and Argo Workflows Manage and monitor workloads at scale About the reader For data analysts and engineers familiar with the basics of machine learning, Bash, Python, and Docker. About the author Yuan Tang is a project lead of Argo and Kubeflow, maintainer of TensorFlow and XGBoost, and author of numerous open source projects. Table of Contents PART 1 BASIC CONCEPTS AND BACKGROUND 1 Introduction to distributed machine learning systems PART 2 PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMS 2 Data ingestion patterns 3 Distributed training patterns 4 Model serving patterns 5 Workflow patterns 6 Operation patterns PART 3 BUILDING A DISTRIBUTED MACHINE LEARNING WORKFLOW 7 Project overview and system architecture 8 Overview of relevant technologies 9 A complete implementation