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Improving operating leverage is about operational resilience, structural operational efficiency, and sustainable revenue growth. Activity-based enterprise non-interest cost management is an important component of enterprise risk adjusted return management methodology. This book builds on the author Kannan Subramanian's earlier book, Event- and Data-Centric Enterprise Risk-Adjusted Return Management, delving in depth into enterprise non-interest operating cost management and operating leverage. Operating Leverage is about managing a bank's capabilities and its capacity to deliver its products and services efficiently. It is not limited to managing operational costs but includes the operational support for the growth of business and for improving profitability. Profit is an absolute measure that denotes the amount of money the bank makes after deducting all expenses. Profitability measures how efficient the bank is at utilizing its resources to generate risk-adjusted returns. The reader can learn to improve risk adjusted operational effectiveness by implementing a nuanced approach to managing performance, risk, control, and cost simultaneously, at the process level. You'll examine how some institutions have implemented activity-based costing in a siloed environment and without enterprise process automation. Many institutions do not have a scientific way of managing non-interest costs. The book explains why hyperautomation, a technology that intelligently automates business processes, is a more advanced and comprehensive way to manage these factors in a holistic and integrated way. Improving Operating Leverage using Hyperautomation is your complete guide to enhancing risk adjusted operational performance through a nuanced approach to performance, risk, control, and costs at the process level. What You Will Learn Define and use the bill of resources to improve operating leverage. Implement activity-based enterprise non-interest cost management using hyperautomation Monitor performance, risk, control, and cost at the process level. Implement time-driven activity-based costing for an enterprise risk-adjusted return model. Who This Book Is For Most banking industry professionals, including senior management teams, consultants, central bankers, financial regulators, software vendors, and Business Process Management Suite/Hyperautomation technology vendors.

After decades of false starts, artificial intelligence (AI) is entering the mainstream of society. That is largely due to the rapid adoption of ChatGPT, a service that responds to almost any natural language question with cogent paragraphs. ChatGPT is the leading example of generative AI -- technology that creates original text, images, video and computer code based on uncovering patterns in training data. The book will explain how generative AI works and how much economic value it could create and will map out the industry value network. For each value network stage, the book will define the industry, estimate its size, growth rate, and profit potential, identify the most successful participants, and explain how they have achieved their success and where they will compete in the future. The book will conclude with a section on what investors and business leaders should do to make an informed decision on where to place their bets.What You Will LearnInsights on how best to assemble the resources – whether by hiring a consultant or bringing on board a generative AI expert -- to build, train, and operate company specific generative AI applicationsHow management can brainstorm, evaluate and execute the right opportunitiesConcepts and processes to enable investors to place bets with the highest risk-adjusted returnsWho This Book is ForBusiness and enterprises seeking to get value from generative AI, current or potential suppliers of technology and services to companies that build generative AI, and venture capitalists and public equity investors seeking to make profitable bets on generative AI companies

Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract.By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning—Specialty certification exam.What You Will LearnBe familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud FormationUnderstand SageMaker, Amazon Comprehend, and Amazon ForecastExecute live projects: from the pre-processing phase to deployment on AWSWho This Book Is ForMachine learning engineers who want to learn AWS machine learning services, and acquire an AWS machine learning specialty certification

Learn how to translate an executable model of your application into running code. This is not a book about theory, good intentions or possible future developments. You'll benefit from translation technology and solid software engineering principles that are demonstrated with concrete examples using an open source tool chain.Models don't deliver enough value if they are not on a direct path to code production. But to waste time building models that are merely pictures of your code doesn't add much value either. In this book, you'll translate detailed, yet platform-independent models that solve real application problems.Using a pragmatic approach, Models to Code quickly dives into two case studies of Executable UML models. The models and code are extensively annotated and illustrate key principles that are emphasized throughout the book. You'll work with code production using'C'as the implementation language and targeting microcomputer classprocessors. This might not be your particular target language or platform, but you can use you can use what you learn here to engineer or re-evaluate your own code translation system to dramatically increase the value of both your modeling and code generation solution. Written by three leading experts, Models to Code is an exceptional resource for producing software by model translation— add it to your library today.What You'll LearnSee how detailed models resolve ambiguity and contradiction common in requirements.Examine how a model can be detailed enough to be executable and testable while remaining platform independentProduce code from a model, leaving the model intact so it can be redeployed on new platforms or adapted to changing software and hardware technology.Implement platform independent model execution rules in platform specific run-time codeWho This Book Is ForModelers and systems engineers on active MBSE projects (using Executable UML or not), projects using Simulink, Matlab, Dymola, MatrixX and other math modelling tools. Any developers with current or past model experience, professors, students, systems engineers, embedded systems developers, or anyone interested in learning more about software modelling.

Learn how to institute Reactive Programming (RP) for your back-end development with Node.js. Up to now, RP has most often been used in front-end development, but with its cutting-edge approach you can also transform your back-end programming. Reactive Programming with Node.js will show you the paradigms of RP, why you should use it, and the variations available to you. You will learn how to use the main libraries necessary to provide an enhanced development experience in Node.js, including RxJS, Bacon.js, Kefir.js, and Highland.js. You will also create a custom library that provides a variety of key features, and learn how to scale up a system developed using RP in Node.js. Ideal for back-end developers with knowledge of Node.js or JavaScript, this book enables you to get up and running with RP in Node.js, and revolutionize your back-end development. What You'll Learn:Review the variations of Reactive programming Use the main libraries that provide this type of development experience in Node.jsCreate a custom libraryScale up a system developed using RP in Node.jsWho This Book Is For: Any back-end developers who understand Node.js or are advanced enough to pick up the basics. Ideal for developers who have an interest in learning about this different programming paradigm that's being used more and more every day.