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.
Most personas and journey maps crash land shortly after takeoff. This step-by-step playbook provides a revolutionary approach to creating these pivotal artifacts in product design through the concept of altitudes. They will guide you in conceptualizing your users through research techniques that directly inform persona characteristics.Discover how to create personas that are free from irrelevant attributes and learn to develop high-altitude personas with sufficient lift to guide product roadmaps and organizational strategy. You'll also master constructing flight paths for journey maps that capture the user's journey at the perfect level of detail, uncovering key moments in their experience. Then, as personas and journeys evolve, you will learn to measure the impact of product experience improvements.With this book as your co-pilot, you'll navigate the creation process with the help of templates and real-world examples reflecting the latest methods for personas and journey maps. The Persona and Journey Map Playbook will provide you with the tools and strategies to create impactful personas and journey maps that will drive user-centered design and product success.
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain's components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you'll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you'll learn-by-be doing, enhancing your career possibilities in today's rapidly evolving landscape.You will: Understand different types of LLMs and how to select the right ones for responsible AI. Structure effective prompts. Master LangChain concepts, such as chains, models, memory, and agents. Apply embeddings effectively for search, content comparison, and understanding similarity. Setup and integrate Pinecone vector database for indexing, structuring data, and search. Build Q & A applications for multiple doc formats. Develop multi-step AI workflow apps using LangChain agents.
For many entrepreneurs, raising finance is the most important moment in their startup journey. For introverts, this can also be the time when they are at their weakest as well as a time when they absolutely, positively, cannot fail. This book will lead the reader to design that pitching process in the most positive and useful way for their abilities and their strengths.Having the confidence of knowing a presenter's message is clearly constructed and the interaction is engineered for maximum impact will make the presenter more relaxed and therefore, more confident. Featuring a series of case studies and a concise writing approach, this book will guide you through a proven method tested with investors in Europe, the USA and Asia.Author Richard Tierney shares his three decades of global experience as a business coach and media producer across many sectors. When done, The Three Slide Pitch Deck will lead you to greater conversion, greater investment, and more successful business ventures.You will Understand that preparation is the key to success. Focus the pitch process to your strengths. Review case studies that show you how to pitch in differing circumstance. See how you and your company can stand out from the crowd.Who This Book is ForIntroverted entrepreneurs, company founders and start-ups seeking finance.
Get up to speed on the key topics required for the Linux Professional Institute's LPIC-3 305 exam, the third in the four part LPIC 3 certification, which covers virtualization and containerization. The wider LPIC-3 certification helps developers and system administrators become experts in a given context of Linux and Open Source solutions. This study companion is designed to sit alongside your studies and certification guides as you prepare for the exam and will take you through the three main areas of Full Virtualization, Container Virtualization as well as VM Deployment and Provisioning. We will undertake a deep dive of all the major topics, from the basics of virtualizations and containers to indepth break down of virtualization solutions like KVN, Xen and Proxmox and well as popular containers like Docker, Kuberentes, Openstack, Terraform, Packer, Vagrant and others with key concepts covered on the exam called out and applied in each chapter of this book, giving you both practice and reinforcement, a far more effective learning tool than rote learning or similar approaches typically enlisted in exam preparation. LPIC-3 Virtualization and Containerization Study Guide will give you the guidance and support as you take the next exciting step in supercharging your Linux skills and knowledge to take the next step in your professional career. You Will: Gain a clear picture about the different types of virtualization in use today. Understand how work with virtualization solutions like KVM and Xen, and manage them using the libvirt API. Become aware of the use of fully enterprise ready virtualization solutions like Proxmox. Learn the technologies that Open Source virtualization, possible
This book takes young readers on an exciting journey into the world of programming through the lens of Python. This engaging and interactive book is designed to introduce young minds to the basic concepts of programming while encouraging their creativity and problem-solving skills. In the introductory chapters, readers discover Python as a friendly and accessible programming language. The book guides them through setting up their programming environment and crafting their initial lines of code, laying the foundation for an exciting coding adventure. As the exploration unfolds, it delves into fundamental programming concepts essential for any budding coder. From variables and data types to loops and conditionals, these building blocks empower readers to create their programs, fostering a solid understanding of the core principles of coding. It seamlessly integrates these concepts with previously learned fundamentals, providing a comprehensive view of Python's capabilities. Fueling creativity, it inspires readers to unleash their imagination through engaging projects. From crafting games to developing useful applications, young coders learn to apply their programming skills in innovative ways, transforming abstract coding concepts into tangible and interactive projects.With a focus on accessibility, engagement, and real-world application, this book paves the way for the next generation of Python enthusiasts. What you will learn: Understand Python programming fundamentals, including syntax, variables, data types, loops, conditionals, and functions Learn to break down complex problems into smaller, manageable tasks and apply coding concepts to find creative solutions. How to create their interactive coding projects using Python. Understand strategies for debugging and troubleshooting common programming problems, which are essential skills for any programmer
Analyse how AI and other cutting-edge technologies affect the complex web of international financial transactions. The global remittance sector and artificial intelligence (AI) work together in a way that is testament to the revolutionary force of innovation in the always changing financial and technology landscape. As we stand at the threshold of a new age, this book aims to explore the layers of complexity underlying the confluence of AI and remittances. Your investigation will go beyond the current state of affairs and into the future, whereby predictive analytics and autonomous financial transactions will fundamentally alter the way that we send and receive money internationally. This process's incorporation of AI technology represents a revolution rather than a simple progression, one that might improve millions of people's lives by streamlining, securing, and lowering the cost of financial transactions. This book breaks down the complex workings of AI prediction models, reveals how blockchain integration may be used to facilitate safe and transparent transactions, and examines how biometric authentication can strengthen the security of financial transactions. It dives into the complexities of regulatory compliance, understanding the difficult balance between innovation and conformity to existing standards.When machines carry out transactions based on complex algorithms and current market circumstances, what does it entail for people and economies? What effect does this change have on how people make financial decisions? This book is an invitation to imagine a future in which financial systems are not just efficient but also naturally intelligent. It is a call to reflect on the moral ramifications, the effects on society, and the obligations that accompany the dawn of a new era of financial opportunity. Remittances are about to undergo a revolution, and you have a crucial role to play in determining how this will play out.
The perfect storm of learning provoked by generative AI is not just about learning how to use the technology to change human patterns of work and life. The technologies are re-orienting how we think we learn, how we communicate with each other, and the economic, social, political, creative, and ethical factors that inform how we navigate human-influenced existence on this planet. This book addresses the need for workers in any industry to take responsibility for learning how to best use generative AI systems in their unique contexts.Generative AI can navigate you towards learning, but also towards conducting research and teaching yourself things; to empower you in reimagining and reinventing how you learn while doing your work. Just like you can regenerate content persistently using generative AI systems, so too can you regenerate what and how you learn. Chapters will prepare you to inform and guide the small team you are a part of, or influence leadership to navigate the territory of leveraging generative AI systems responsibly.Besides pointing to all the more obvious benefits of learning how to use generative AI systems more effectively, this book provides use cases, research and educational theory to propose that interacting with the technology leads to a number of unanticipated learning outcomes. These outcomes challenge the very way in which we have come to learn, what we have learned, and what we may need to unlearn. As generative AI becomes increasingly integrated within workplace environments at some point or other we will each reach a critical point of having to decide if we are going to use the technology and how.
Journey through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python.The book starts by reviewing financial concepts, such as analyzing different asset types like stocks, options, and portfolios. It then delves into the crux of stochastic finance, providing a glimpse into the probabilistic nature of financial markets. You'll look closely at probability theory, random variables, Monte Carlo simulation, and stochastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You'll get a glimpse of two vital modelling tools used throughout the book - stochastic calculus and stochastic differential equations (SDE). Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.
Explore the intersection between unmanned aerial vehicles (UAVs) and the evolving landscape of smart cities. With the increasing integration of technology into urban environments, there is a growing need to understand how UAV swarms can contribute to the safety, efficiency, and resilience of these complex urban ecosystems. The book aims to provide a technical understanding of UAV swarms and their applications within the context of smart cities. It begins by laying the groundwork with an introduction to UAV swarms and smart cities, establishing the foundational concepts and motivations behind their integration. As the book progresses, it delves into various aspects of smart cities, exploring concepts, technologies, and challenges inherent in their development and operation. This includes discussions on cloud computing, cybersecurity, machine learning applications, surveillance and monitoring systems, urban planning, and infrastructure management. It also examines the integration of IoT devices with UAV swarms, highlighting the synergies between these emerging technologies and their potential impact on urban environments.The book examines cutting-edge topics such as edge computing, blockchain applications, 5G integration, and augmented reality/virtual reality (AR/VR) visualization techniques in the context of UAV swarm operations. It concludes with reflections on innovations and future directions, offering insights into the evolving landscape of UAV swarm technologies and their implications for the protection and advancement of smart cities. The book serves as a comprehensive guide for researchers, practitioners, and policymakers interested in understanding the technical, social, and economic dimensions of UAV swarm technology within the context of smart city development and management.
Discover how Artificial Intelligence (AI) is transforming the fields of software development, testing, and IT operations by enhancing efficiency, reducing human error, and accelerating processes. This book showcases the practical applications of AI-driven tools, such as automating coding, testing, and operational tasks, predicting potential issues, and optimizing performance.Aimed at digital leaders, practitioners, and customers, this book provides strategic insights and actionable guidance on how to integrate AI technologies to boost productivity, enhance product quality, and streamline development cycles. It serves as a comprehensive guide for those looking to leverage AI to drive innovation, cut costs, and stay competitive in an ever-evolving technological landscape.You'll explore how AI can be integrated into software development, testing, and IT operations to improve efficiency, accuracy, and speed. Through real-world use cases, you'll see how AI-driven tools can automate tasks, reduce human error, and improve processes across the development lifecycle. AI Integration in Software Development and Operations offers actionable insights on using AI to accelerate innovation, enhance product quality, and optimize costs in your modern software and IT environments. You will: Review the SDLC lifecycle, DevOps, SRE and accompanying topics Understand machine learning basics, AI techniques, and data preprocessing for DevOps Explore how AI integration into all phases of SDLC boosts productivity, increases effectiveness, and reduces human error Gain a familiarity with AI tools, their use cases, and the value in integrating them
Master Oracle Database administration in both on-premises and cloud environments. This new edition covers the tasks you'll need to perform to keep your databases tuned and performing, and includes new, important innovations with AI Vector Search, JSON Duality Views, and Select AI. Since Oracle Database 23ai offers a choice of platforms with on-premises and cloud, the book also includes administrative tasks specific to cloud environments, including the Oracle Autonomous Database running in the Oracle Cloud Infrastructure. New in this edition is help for DBAs who are becoming involved in data management, and a look at the idea of a converged database and what that means in handling various data types and workloads. The book covers some of the machine learning features now in Oracle and shows how the same SQL that you know for database administration also helps you with data management tasks. The information in this book helps you to apply the right solution at the right time, mitigating risk and making robust choices that protect your data and avoid midnight phone calls.Data management is increasingly a DBA function, and DBAs are often called upon for help in getting data loaded into analytics environments such as a data lakehouse or a data mesh. This book addresses this fast-growing new role for database administrators and helps you build on your existing knowledge to make the transition into a new skill set that is in high demand. You'll learn how to look at data optimization from the standpoint of data analysis and machine learning so that you can be seen as a key player in preparing your organization's data for those type of activities. You'll know how to pull back information from a combination of relational tables and JSON structures. You'll become familiar with the tools that Oracle Database provides to make analytics easier and more straightforward. And you'll learn simpler ways to manage time-based tables that eliminate the need for painfully creating triggers to track the history of row changes over time.This book builds your skills as an Oracle Database administrator with the aim of helping you to be seen as a key player in data management as your organization pivots toward cloud computing and a greater use of machine learning and analytics technologies.What You Will Learn Configure and manage Oracle 23ai databases both on-premises and in the cloud Meet your DBA responsibilities in the Oracle Cloud and with Database Cloud Services Leverage converged database capabilities to manage different workloads, structured and unstructured data Perform administrative tasks for Autonomous Database dedicated environments Perform DBA tasks and effectively use data management tools Migrate from on-premises to the Oracle Cloud Infrastructure Troubleshoot issues with Oracle 23ai databases and quickly solve performance problems Architect cloud, on-premises, hybrid, and multi-cloud database environments
This book discusses deep learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on deep learning techniques and shows how to apply them across a wide range of practical scenarios. The book begins with an introduction to the core concepts of deep learning. It delves into topics such as transfer learning, multi-task learning, and end-to-end learning, providing insights into various deep learning models and their real-world applications. Next, it covers neural networks, progressing from single-layer perceptrons to multi-layer perceptrons, and solving the complexities of backpropagation and gradient descent. It explains optimizing model performance through effective techniques, addressing key considerations such as hyperparameters, bias, variance, and data division. It also covers convolutional neural networks (CNNs) through two comprehensive chapters, covering the architecture, components, and significance of kernels implementing well-known CNN models such as AlexNet and LeNet. It concludes with exploring autoencoders and generative models such as Hopfield Networks and Boltzmann Machines, applying these techniques to a diverse set of practical applications. These applications include image classification, object detection, sentiment analysis, COVID-19 detection, and ChatGPT. By the end of this book, you will have gained a thorough understanding of deep learning, from its fundamental principles to its innovative applications, enabling you to apply this knowledge to solve a wide range of real-world problems. What You Will Learn What are deep neural networks? What is transfer learning, multi-task learning, and end-to-end learning? What are hyperparameters, bias, variance, and data division? What are CNN and RNN?
This book is the first comprehensive guide to the intersection of data governance and machine learning (ML) projects. As ML applications proliferate, the quality, reliability, and ethical use of data is central to their success, which gives ML data governance unprecedented significance. However, adapting data governance principles to ML systems presents unique, complex challenges. Author Aditya Nandan Prasad equips you with the knowledge and tools needed to navigate this dynamic landscape effectively. Through this guide, you will learn to implement robust and responsible data governance practices, ensuring the development of sustainable, ethical, and future-proofed AI applications.The book begins by covering fundamental principles and practices of underlying ML applications and data governance before diving into the unique challenges and opportunities at play when adapting data governance theory and practice to ML projects, including establishing governance frameworks, ensuring data quality and interpretability, preprocessing, and the ethical implications of ML algorithms and techniques, from mitigating bias in AI systems to the importance of transparency in models.Monitoring and maintaining ML systems performance is also covered in detail, along with regulatory compliance and risk management considerations. Moreover, the book explores strategies for fostering a data-driven culture within organizations and offers guidance on change management to ensure successful adoption of data governance initiatives. Looking ahead, the book examines future trends and emerging challenges in ML data governance, such as Explainable AI (XAI) and the increasing complexity of data.What You Will Learn Comprehensive understanding of machine learning and data governance, including fundamental principles, critical practices, and emerging challenges Navigating the complexities of managing data effectively within the context of machine learning projects Practical strategies and best practices for implementing effective data governance in machine learning projects Key aspects such as data quality, privacy, security, and ethical considerations, ensuring responsible and effective use of data Preparation for the evolving landscape of ML data governance with a focus on future trends and emerging challenges in the rapidly evolving field of AI and machine learning
Explore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs).The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience.By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks.What You Will Learn Grasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMs Implement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examples Know the techniques for data pre-processing, model selection, and customization to optimize machine learning models Apply machine learning and neural network techniques in various professional scenarios
This book is your comprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologies-such as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3-to streamline the development, training, and deployment of deep learning models.Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale.By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today's data-driven landscape.What You Will Learn Maximize AWS services for scalable and high-performance deep learning architectures Harness the capacity of PyTorch and TensorFlow for advanced neural network development Utilize PySpark for efficient distributed data processing on AWS Orchestrate complex workflows with Apache Airflow for seamless data processing, model training, and deployment
Delve into the evolution of healthcare technologies, exploring their impact on patient care and management. This book provides a comprehensive exploration of the industrial revolution in healthcare.In this book, you'll cover the fundamentals of artificial intelligence (AI) in healthcare, including an overview of AI and machine learning, applications in healthcare domains, and challenges and opportunities in AI implementation. It progresses to explore integration of AI and IoT in Healthcare 4.0, discussing synergies, real-time data analysis, and future trends in telemedicine. The book also addresses critical aspects such as data security and privacy, focusing on regulations, standards, and strategies for ensuring data protection. Practical applications of AI and IoT in remote patient monitoring, disease diagnosis, and healthcare operations management are thoroughly examined, alongside ethical and legal considerations in Healthcare 4.0. The final chapters offer insights into emerging trends, potential challenges, and recommendations for successfully adopting AI and IoT in healthcare.Readers will gain a comprehensive understanding of how AI and IoT are revolutionizing healthcare, from enhancing patient outcomes and operational efficiencies to navigating the ethical and legal landscapes of data privacy. This book equips healthcare professionals, policymakers, and technology enthusiasts with the knowledge to navigate and leverage the transformative potential of Healthcare 4.0 technologies effectively.What You Will Learn Explore the integration of AI with IoT Technologies in Healthcare 4.0 Gain insights into the ethical and legal considerations surrounding AI and IoT implementations in healthcare Discover case studies and practical examples illustrating the transformative impact of AI and IoT on patient care
Explore the transformative benefits of Infrastructure as Code (IaC) and understand why Terraform is the go-to tool for managing cloud infrastructure efficiently. This book is your ultimate guide to mastering Terraform on Google Cloud Platform, providing you with the tools and knowledge to automate and optimize your cloud infrastructure with confidence.You'll start by reviewing the traditional approach to managing infrastructure, common challenges, and the benefits of adopting IaC and Terraform. You'll then learn how to install Terraform on various operating systems and get familiar with its configuration language, basic commands, and syntax. The book then turns to provisioning infrastructures on GCP, managing secrets and enhancing security, and concludes with integrating collaboration and DevOps using Terraform.The power of cloud platforms is growing, providing numerous ways to manage infrastructures more efficiently. While the traditional approach to infrastructure management works well on a smaller scale, it becomes a challenge when dealing with complex or extensive projects. From installation and configuration to advanced provisioning and security practices, this book provides a clear, step-by-step approach to mastering Terraform.You will: Explore providers, variables, modules, state management, and dependencies. Master encryption methods and IAM policies. Secure remote state management to protect sensitive data and ensure compliance. Discover frameworks, tools, and best practices for testing IaC code. Automate provisioning with CI/CD pipelines. Provision a comprehensive suite of infrastructure resources on Google Cloud Platform.
In today's global market, supply chain management has become more critical than ever. This book delves into the strategic, operational, and technological aspects of supply chains, offering insights and practical solutions to create a seamless and resilient supply network. The main topics covered are production planning and inventory control, logistics and distribution, sustainability, risk management and future trends.Companies are facing unprecedented challenges, from geopolitical uncertainties and trade wars to natural disasters and global pandemics. These disruptions can have significant impacts on the supply chain, leading to delays, increased costs, and loss of customer trust. By addressing these critical areas, this book aims to equip you with the knowledge and tools needed to optimize your supply chains, drive innovation, and achieve sustainable success in a dynamic and challenging environment.Whether you are a seasoned supply chain professional or a newcomer to the field, From Source to Shelf provides valuable insights and practical guidance to help you master the art of supply chain management.You will: Understand the fundamental components and functions of supply chain management. Develop and implement effective supply chain strategies that align with overall business objectives. Navigate the complexities of modern supply chain management. Leverage cutting-edge technologies and implement sustainable and resilient practices.
Navigate the complex landscape of Artificial Intelligence (AI) governance and model risk management using a holistic approach encompassing people, processes, and technology. This book provides practical guidance, oversight structure and centers of excellence, and actionable insights for organizations seeking to harness the power of AI responsibly, ethically, and transparently. By addressing the technical, ethical, and societal dimensions of AI governance, organizations will be empowered to build trustworthy AI systems that benefit both their bottom line and the broader community.Featuring successful mitigating controls based on proven use cases, the book underscores the importance of aligning AI strategy with AI governance, striking a balance between AI innovation, risk mitigation as well as broader business goals. You'll receive pointers for designing a well-governed AI development lifecycle, emphasizing transparency, accountability, and continuous monitoring throughout the AI development lifecycle. This book highlights the importance of collaboration between stakeholders, i.e., boards of directors, CxOs, corporate counsel, compliance officers, audit executives, data scientists, developers, validators, etc.You'll gain practical advice on addressing the challenges related to the ownership of AI-generated content and models, stressing the need for legal frameworks and international collaboration. You'll also learn the importance of auditing AI systems, developing protocols for rapid response in case of AI-related crises, and building capacity for AI actors through education. Principles of AI Governance and Model Risk Management demonstrates its value-added uniqueness by detailing a strategy to ensure a cohesive approach to managing AI-related risks, global compliance, policy, privacy, and AI-human collaboration and oversight.
Explore Generative AI and understand its key concepts, architecture, and tangible business use cases. This book will help you develop the skills needed to use SAP AI Core service features available in the SAP Business Technology Platform.You'll examine large language model (LLM) concepts and gain the practical knowledge to unleash the best use of Gen AI. As you progress, you'll learn how to get started with your own LLM models and work with Generative AI use cases. Additionally, you'll see how to take advantage Amazon Bedrock stack using AWS SDK for ABAP.To fully leverage your knowledge, Generative AI with SAP and Amazon Bedrock offers practical step-by-step instructions for how to establish a cloud SAP BTP account model and create your first GenAIartifacts. This work is an important prerequisite for those who want to take full advantage of generative AI with SAP.You will: Master the concepts and terminology of artificial intelligence and GenAI Understand opportunities and impacts for different industries with GenAI Become familiar with SAP AI Core, Amazon Bedrock, AWS SDK for ABAP and develop your firsts GenAI projects Accelerate your development skills Gain more productivity and time implementing GenAI use cases
anage project objectives between supplier and customer. This book introduces a dedicated method for suppliers to follow to ensure supplier and customer expectations are successfully meet. The method integrates a day-to-day high awareness, urgency and focus on the management of all objectives within the project to provide maximum benefit for an organization.You'll learn that formal targets are routinely established at the start of a project and captured in a contract or project charter. Customer expectations, such as timelines and budget, are often pitted against internal day-to-day challenges, such as cost increases and other unexpected changes. These challenges can seem even more daunting as a project progresses.To combat this, you'll follow detailed instructions on how handle such potential roadblocks by applying the established method. Each chapter expands the dedicated method itself and provides insight into this philosophy. In the end you'll have all the necessary prerequisites for a successful implementation of these principles within your organization.You will: Handle day-to-day challenges on a project, such as communication, unexpected changes, cost increases, and more Facilitate the formal and informal targets and project goals Balance customer and supplier expectations
In the fast-evolving digital landscape, understanding the potential of generative AI is a strategic advantage. This book can serve as an easy to read introduction to the topic of the transformative power of AI in content creation, customer engagement, and operational efficiency. By deciphering complex AI concepts into practical insights, we empower decision-makers to envision innovative strategies, foster cross-industry collaborations, and navigate ethical considerations. The book will help executives and business decision makers to harness the immense potential of generative AI responsibly, ensuring data integrity and compliance while fostering a competitive edge. The book is focused on (1) Explaining in jargon-free language what Generative AI, and AI in general, (2) What problems they solve, and (3) What technologies make them possible.What You Will LearnHow generative AI models are built, how they generate new data or content, and the underlying algorithms powering these processesVarious practical applications of generative AI in business contextsThe challenges that could arise during the integration of generative AI into business processesWho This Book is ForThis book is meant to be bought and read by busy executives and business leaders
This book explores three crucial topics for cybersecurity professionals: artificial intelligence (AI), automation, and active cyber defense (ACD). The Cybersecurity Trinity will provide cybersecurity professionals with the necessary background to improve their defenses by harnessing the combined power of these three concepts. The book is divided into four sections, one addressing each underlying concept and the final section discussing integrating them to harness their full potential.With the expected growth of AI and machine learning (ML), cybersecurity professionals must understand its core concepts to defend AI and ML-based systems. Also, most cybersecurity tools now incorporate AI and ML. However, many cybersecurity professionals lack a fundamental understanding of AI and ML. The book's first section aims to demystify AI and ML for cybersecurity practitioners by exploring how AI and ML systems work, where they are vulnerable, and how to defend them.Next, we turn our attention to security automation. Human-centered cyber defense processes cannot keep pace with the threats targeting organizations. Security automation can help defenders drastically increase the speed of detection and response. This section will discuss core use cases that security teams can implement, including intelligence processing, incident triage, detection, and response. This section will end with strategies for a successful security automation implementation and strategies that can lead to failure.Accelerating the defense is but one side of the equation. Defenders can also implement ACD methods to disrupt and slow the attacker. Of course, ACD spans a broad spectrum, including some that could raise legal and ethical concerns. This section will explore some ACD methods and discuss their applicability, as well as the need to include business, legal, and ethical considerations when implementing them.Security teams often treat AI, automation, and ACD as disparate solutions, addressing specific problems. However, there is much overlap, and security teams must develop a cohesive approach to realize the full potential. The last section combines these three concepts to form a comprehensive strategy. The resulting strategy will have AI as the foundation, incorporating automation to speed up defense and ACD to disrupt the attacker. What You Will Learn: Understand the many uses of AI and ML and the concepts underpinning these technologies. Learn how to protect AI and ML systems by recognizing the vulnerabilities throughout their lifecycle. Integrate AI and ML-based systems to enhance cybersecurity. Develop security automation processes to enhance situation awareness, speed the time to respond, and increase the bandwidth of the limited security operations staff. Develop an ACD strategy to slow the attackers while minimizing legal and ethical concerns. Design a comprehensive strategy with AI as the foundation, incorporating automation to speed up defense and ACD to disrupt the attacker.
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