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Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning ServiceKey Features:Automate complete machine learning solutions using Microsoft AzureUnderstand how to productionize machine learning modelsGet to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learningBook Description:Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You'll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.Throughout the book, you'll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You'll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework.By the end of this Azure Machine Learning book, you'll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.What You Will Learn:Train ML models in the Azure Machine Learning serviceBuild end-to-end ML pipelinesHost ML models on real-time scoring endpointsMitigate bias in ML modelsGet the hang of using an MLOps framework to productionize modelsSimplify ML model explainability using the Azure Machine Learning service and Azure InterpretWho this book is for:Machine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered.

Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problemsPurchase of the print or Kindle book includes a free eBook in the PDF formatKey FeaturesExplore unique recipes for financial data processing and analysis with PythonApply classical and machine learning approaches to financial time series analysisCalculate various technical analysis indicators and backtest trading strategiesBook DescriptionPython is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.What you will learnPreprocess, analyze, and visualize financial dataExplore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning modelsUncover advanced time series forecasting algorithms such as Meta's ProphetUse Monte Carlo simulations for derivatives valuation and risk assessmentExplore volatility modeling using univariate and multivariate GARCH modelsInvestigate various approaches to asset allocationLearn how to approach ML-projects using an example of default predictionExplore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphetWho this book is forThis book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.Table of ContentsAcquiring Financial DataData PreprocessingVisualizing Financial Time SeriesExploring Financial Time Series DataTechnical Analysis and Building Interactive DashboardsTime Series Analysis and ForecastingMachine Learning-Based Approaches to Time Series ForecastingMulti-Factor ModelsModelling Volatility with GARCH Class ModelsMonte Carlo Simulations in FinanceAsset AllocationBacktesting Trading StrategiesApplied Machine Learning: Identifying Credit DefaultAdvanced Concepts for Machine Learning ProjectsDeep Learning in Finance

The exponential growth of data combined with the need to derive real-time business value is a critical issue today. An event-driven data mesh can power real-time operational and analytical workloads, all from a single set of data product streams. With practical real-world examples, this book shows you how to successfully design and build an event-driven data mesh. Building an Event-Driven Data Mesh provides: Practical tips for iteratively building your own event-driven data mesh, including hurdles you'll experience, possible solutions, and how to obtain real value as soon as possible Solutions to pitfalls you may encounter when moving your organization from monoliths to event-driven architectures A clear understanding of how events relate to systems and other events in the same stream and across streams A realistic look at event modeling options, such as fact, delta, and command type events, including how these choices will impact your data products Best practices for handling events at scale, privacy, and regulatory compliance Advice on asynchronous communication and handling eventual consistency

This book provides an up-to-date account of current research in quantum information theory, at the intersection of theoretical computer science, quantum physics, and mathematics. The book confronts many unprecedented theoretical challenges generated by infinite dimensionality and memory effects in quantum communication. The book will also equip readers with all the required mathematical tools to understand these essential questions.

Researchers from the Homeland Security Operational Analysis Center have developed a methodology for understanding and prioritizing cybersecurity risk in election infrastructure to assist state and local election officials.

This open access book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by Trinity College Dublin, DARIAH-EU and the European Commission.

This issues contents includes: Editor's Letter>Modeling NFT Investor Behavior Using Belief Dissensus>Modelling & Simulation of a Rivet Shaving Process for the Protection of the Aerospace Industry Against Cyber-threats Martin Praddaude, Nicolas Hogrel, Matthieu Gay, Ulrike Baumann, and Adrien Bécue Complex Simulation Workflows in Containerized High-Performance Environment>Augmented Reality Implementation for Comfortable Adaptation of Disabled Personnel to the Production Workplace>Designing an Emergency Information System for an Emergency Information System for Catastrophic Natural Situations>A Return to "A Complexity Context to Classroom Interactions and Climate Impact on Achievement"Joseph Cochran and Liz Johnson

This Book Includes: Machine Learning: A Comprehensive, Step-by-Step Guide to Learning and Understanding Machine Learning Concepts, Technology and Principles for BeginnersMachine Learning: A Comprehensive, Step-by-Step Guide to Intermediate Concepts and Techniques in Machine LearningMachine Learning: A Comprehensive, Step-by-Step Guide to Learning and Applying Advanced Concepts and Techniques in Machine LearningMachine Learning: A Complete Exploration of Highly Advanced Machine Learning Concepts, Best Practices and TechniquesBuy the Paperback version of this book, and get the Kindle eBOOK version for FREEGraphics in this book are printed in black and white.Machines are created to make work easier for us, but so many have seen machines as a major barrier due to their supposed technicality of machines.Are you a novice trying to understand the basics of machine?Do you have prior knowledge and you wish to acquire further understanding about tensorFlow, scikit- learn, algorithms, decision trees, random forest, deep learning or neural networks?Are you even a pro and you wish to add to your knowledge?This book is all you need. This painstakingly compiled manuscript unravels the rudiments and generality of machine learning. It is total and all encompassing with accurate and concise principles of machine learning. This quintessential book comprises modules that cut across various level of knowledge in machine learning. It is an exquisite material that grants you practical knowledge in machines. It weighs more than mere words, it is gold in manuscript.You might not know how much you know or how much you need to know until you avail yourself with essential materials. This book is not one of all you need to understand machine learning; it is all you need to uncover the full scope of learning machines.Technicality is very relative when you have the right knowledge. Stay ahead; make a choice that will last. Would You Like To Know More?Scroll to the top of the page and select the buy now button.

Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Because rather than simply report what has happened, GA4's new cloud integrations enable more data activationlinking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations.Author Mark Edmondson, Google Developer Expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get guidance on how to implement them.You'll learn:How Google Cloud integrates with GA4The potential use cases that GA4 integrations can enableSkills and resources needed to create GA4 integrationsHow much GA4 data capture is necessary to enable use casesThe process of designing dataflows from strategy though data storage, modeling, and activation

Data storage has grown such that distributed storage over a number of systems is now commonplace. This has given rise to an increase in the complexity of ensuring data loss does not occur, particularly where failure is due to the failure of individual nodes within the storage system. Redundancy was the main tool to combat this, but with huge increases in data, minimization of the overhead associated with this technique caused major concern. In a large data center, a third concern arose, namely the need for efficient recovery from the failure of a single storage unit. In this monograph, the authors give a comprehensive overview of the role of differing types of codes in addressing the issues in large distributed storage systems. They introduce the reader to regenerative codes, locally recoverable codes and locally regenerative codes; the three main classes of codes used in such systems. They give an exhaustive overview of how these codes were created, their uses and the developments and improvements of the codes in the last decade. This in-depth review gives the reader an accessible and complete overview of the modern codes used in distributed storage systems today. It is a one-stop source for students, researchers and practitioners working on any such system.