The book simplifies the complexities of cloud transition and offers a clear, actionable roadmap for organizations moving from SAP BW or BW/4HANA to SAP Datasphere and SAP Analytics Cloud (as part of SAP Business Data Cloud), particularly in alignment with S/4HANA transformation. Whether you are assessing your current landscape, building a business case with ROI analysis, or creating a phased implementation strategy, this book delivers both technical and strategic guidance. It highlights short- and long-term planning considerations, outlines migration governance, and provides best practices for managing projects across hybrid SAP environments. From identifying platform gaps to facilitating stakeholder discussions, this book is an essential resource for anyone involved in the analytics modernization journey. You Will: [if !supportLists] · [endif] Learn how to assess your current SAP BW or BW/4HANA landscape and identify key migration drivers [if !supportLists] · [endif] Understand best practices for leveraging out-of-the-box cloud features and AI/ML capabilities [if !supportLists] · [endif] A step-by-step approach to planning and executing the move to SAP Business Data Cloud (Mainly SAP Datasphere and SAP Analytics Cloud) This book is for: SAP BW/BW4HANA Customers, SAP Consultants, Solution Architects and Enterprise Architects

Unlock the power of Oracle Database 23AI and Autonomous Database Serverless (ADB-S) with this comprehensive guide to the latest innovations in performance, security, automation, and AI-driven optimization. As enterprises embrace intelligent and autonomous data platforms, understanding these capabilities is essential for data architects, developers, and DBAs. Explore cutting-edge features such as vector data types and AI-powered vector search, revolutionizing data retrieval in modern AI applications. Learn how schema privileges and the DB_DEVELOPER_ROLE simplify access control in multi-tenant environments. Dive into advanced auditing, SQL Firewall, and data integrity constraints to strengthen security and compliance. Discover AI-driven advancements like machine learning-based query execution, customer retention prediction, and AI-powered query tuning. Additional chapters cover innovations in JSON, XML, JSON-Relational Duality Views, new indexing techniques, SQL property graphs, materialized views, partitioning, lock-free transactions, JavaScript stored procedures, blockchain tables, and automated bigfile tablespace shrinking. What sets this book apart is its practical focus—each chapter includes real-world case studies and executable scripts, enabling professionals to implement these features effectively in enterprise environments. Whether you're optimizing performance or aligning IT with business goals, this guide is your key to building scalable, secure, and AI-powered solutions with Oracle 23AI and ADB-S. What You Will Learn Explore Oracle 23AI's latest features through real-world use cases Implement AI/ML-driven optimizations for smarter, autonomous database performance Gain hands-on experience with executable scripts and practical coding examples Strengthen security and compliance using advanced auditing, SQL Firewall, and blockchain tables Master high-performance techniques for query tuning, in-memory processing, and scalability Revolutionize data access with AI-powered vector search in modern AI workloads Simplify user access in multi-tenant environments using schema privileges and DB_DEVELOPER_ROLE Model and query complex data using JSON-Relational Duality Views and SQL property graphs Who this Book is For Database architects, data engineers, Oracle developers, and IT professionals seeking to leverage Oracle 23AI’s latest features for real-world applications

This book is a comprehensive guide designed to equip you with the practical skills and knowledge necessary to tackle real-world data challenges using Open Source solutions. Focusing on 10 real-world data engineering projects, it caters specifically to data engineers at the early stages of their careers, providing a strong foundation in essential open source tools and techniques such as Apache Spark, Flink, Airflow, Kafka, and many more. Each chapter is dedicated to a single project, starting with a clear presentation of the problem it addresses. You will then be guided through a step-by-step process to solve the problem, leveraging widely-used open-source data tools. This hands-on approach ensures that you not only understand the theoretical aspects of data engineering but also gain valuable experience in applying these concepts to real-world scenarios. At the end of each chapter, the book delves into common challenges that may arise during the implementation of the solution, offering practical advice on troubleshooting these issues effectively. Additionally, the book highlights best practices that data engineers should follow to ensure the robustness and efficiency of their solutions. A major focus of the book is using open-source projects and tools to solve problems encountered in data engineering. In summary, this book is an indispensable resource for data engineers looking to build a strong foundation in the field. By offering practical, real-world projects and emphasizing problem-solving and best practices, it will prepare you to tackle the complex data challenges encountered throughout your career. Whether you are an aspiring data engineer or looking to enhance your existing skills, this book provides the knowledge and tools you need to succeed in the ever-evolving world of data engineering. You Will Learn: The foundational concepts of data engineering and practical experience in solving real-world data engineering problems How to proficiently use open-source data tools like Apache Kafka, Flink, Spark, Airflow, and Trino 10 hands-on data engineering projects Troubleshoot common challenges in data engineering projects Who is this book for: Early-career data engineers and aspiring data engineers who are looking to build a strong foundation in the field; mid-career professionals looking to transition into data engineering roles; and technology enthusiasts interested in gaining insights into data engineering practices and tools.

In the evolving landscape of SAP development, performance is no longer just a nice-to-have—it's a necessity. With the power of SAP HANA and the enhancements introduced in ABAP 7.5, developers are now equipped to rethink how applications are built, executed, and optimized. This book is your guide to that transformation. We begin by understanding the core shift: moving data-intensive operations directly into the HANA database. When implemented correctly, this "code pushdown" philosophy dramatically reduces data transfer and processing overhead. AMDP (ABAP Managed Database Procedures), our in-database processing engine, enables us to write complex logic directly in SQLScript, harnessing HANA’s parallel processing capabilities. We focus on crafting efficient AMDP procedures by adopting set-based operations and minimizing unnecessary data movement. Next, we explore Core Data Services (CDS) Views, our go-to data modeling tool. CDS Views are not just simple database views; they act as semantic layers that define how our applications interact with data. We learn to create optimized CDS Views by leveraging associations, annotations, and table functions, enabling us to build reusable, high-performance data models. These views simplify complex queries, improve data consistency, and enhance application flexibility. We then turn to Native SQL, our direct line to the HANA database. While AMDP and CDS Views provide powerful abstractions, Native SQL offers ultimate control for specialized tasks. We embed Native SQL within AMDP procedures to access database-specific features and fine-tune performance for critical operations. Along the way, we apply best practices for writing efficient queries, with a strong focus on indexing, join strategies, and precise data filtering. Throughout this journey, we emphasize the importance of rigorous testing and proactive monitoring. Just like a race car undergoes extensive testing before hitting the track, our ABAP applications require careful validation to ensure accuracy and optimal performance. We explore techniques for unit testing AMDP procedures, validating CDS Views, and monitoring query performance. We also look at strategies for detecting and addressing potential bottlenecks before they affect end users. SAP ABAP 7.5 Optimization for HANA is not just about writing faster code—it’s about fundamentally rethinking how we develop applications. By embracing code pushdown, leveraging AMDP, CDS Views, and Native SQL, and implementing robust testing and monitoring strategies, we build ABAP applications that are not only faster, but also more scalable, maintainable, and adaptable to the ever-evolving demands of modern business. You Will: Learn how to implement the "code pushdown" philosophy, moving data-intensive operations directly into the HANA database to reduce data transfer and processing overhead Understand to create optimized CDS Views, leveraging associations, annotations, and table functions to build reusable, high-performance data models that simplify complex queries and improve data consistency. Explore how to write complex logic directly in SQLScript using AMDP, harnessing HANA's parallel processing capabilities, and using Native SQL for specialized tasks, accessing database-specific features to optimize performance. This Book is For: ABAP Developers, SAP Consultants and Architects and IT Managers and Technical Leads

Unlock the secrets of practical data analysis with the Data Flow Map framework—a game-changing approach that transcends tools and platforms. This book isn’t just another programming manual; it’s a guide to thinking and communicating about data at a higher level. Whether you're working with spreadsheets, databases, or AI-driven models, you'll learn how to express your analytics in clear, common language that anyone can understand. In today’s data-rich world, clarity is the real challenge. Technical details often obscure insights that could drive real impact. The Data Flow Map framework simplifies complexity into three core motions: source, focus, and build. The first half of the book explores these concepts through illustrations and stories. The second half applies them to real-world datasets using tools like Excel, SQL, and Python, showing how the framework works across platforms and use cases. A vital resource for analysts at any level, this book offers a practical, tool-agnostic approach to data analysis. With hands-on examples and a universal mental model, you’ll gain the confidence to tackle any dataset, align your team, and deliver insights that matter. Whether you're a beginner or a seasoned pro, the Data Flow Map framework will transform how you approach data analytics. What You Will Learn Grasp essential elements applicable to every data analysis workflow Adapt quickly to any dataset, tool, or platform Master analytic thinking at a higher level Use analytics patterns to better understand the world Break complex analysis into manageable, repeatable steps Iterate faster to uncover deeper insights and better solutions Communicate findings clearly for better decision-making Who This Book Is For Aspiring data professionals and experienced analysts, from beginners to seasoned data engineers, focused on data collection, analysis, and decision making

Engineering Lakehouses with Open Table Formats introduces the architecture and capabilities of open table formats like Apache Iceberg, Apache Hudi, and Delta Lake. The book guides you through the design, implementation, and optimization of lakehouses that can handle modern data processing requirements effectively with real-world practical insights. What this Book will help me do Understand the fundamentals of open table formats and their benefits in lakehouse architecture. Learn how to implement performant data processing using tools like Apache Spark and Flink. Master advanced topics like indexing, partitioning, and interoperability between data formats. Explore data lifecycle management and integration with frameworks like Apache Airflow and dbt. Build secure lakehouses with regulatory compliance using best practices detailed in the book. Author(s) Dipankar Mazumdar and Vinoth Govindarajan are seasoned professionals with extensive experience in big data processing and software architecture. They bring their expertise from working with data lakehouses and are known for their ability to explain complex technical concepts clearly. Their collaborative approach brings valuable insights into the latest trends in data management. Who is it for? This book is ideal for data engineers, architects, and software professionals aiming to master modern lakehouse architectures. If you are familiar with data lakes or warehouses and wish to transition to an open data architectural design, this book is suited for you. Readers should have basic knowledge of databases, Python, and Apache Spark for the best experience.

Are you ready to turn your data into powerful insights and make smarter business decisions? Microsoft Power BI Step by Step is your hands-on guide to mastering one of todays most in-demand business intelligence tools. Written by certified Power BI experts Nuric Ugarte and José Rafael Escalanteleaders in the Power BI community, with years of real-world consulting and teaching experiencethis book takes you from your very first steps in Power BI all the way to advanced data modeling, DAX calculations, and sharing interactive reports with your team. Whether youre a business analyst, data professional, Excel power user, or IT decision-maker, youll find clear, step-by-step instructions and practical exercises that make learning Power BI approachable and effective. Youll learn how to connect to a wide range of data sources, clean and transform your data, create stunning visualizations, and collaborate securely in the Power BI Service. Plus, youll discover how to use the latest features, including Copilot, to streamline your workflow and get answers faster. If you want to build your data skills, impress your organization, and unlock the full potential of Power BI, this is the book you need to get thereone step at a time.

Bioinformatics with Python Cookbook provides a practical, hands-on approach to solving computational biology challenges with Python, enabling readers to analyze sequencing data, leverage AI for bioinformatics applications, and design robust computational pipelines. What this Book will help me do Perform comprehensive sequence analysis using Python libraries for refined data interpretation. Configure and run bioinformatics workflows on cloud environments for scalable solutions. Apply advanced data science practices to analyze and visualize bioinformatics data. Explore the integration of AI tools in processing multimodal biological datasets. Understand and utilize bioinformatics databases for research and development. Author(s) Shane Brubaker is an experienced computational biologist and software developer with a strong background in bioinformatics and Python programming. With years of experience in data analysis and software engineering, Shane has authored numerous solutions for real-world bioinformatics issues. He brings a practical, example-driven teaching approach, aimed at empowering readers to apply techniques effectively in their work. Who is it for? This book is suitable for bioinformatics professionals, data scientists, and software engineers with moderate experience seeking to expand their computational biology knowledge. Readers should have basic understanding of biology, programming, and cloud tools. By engaging with this book, learners can advance their skills in Python and bioinformatics to address complex biological data challenges effectively.

"AI Native LLM Security" is your essential guide to understanding and securing large language models and AI systems. With a focus on implementing practical strategies and leveraging frameworks like OWASP Top 10, this book equips professionals to identify and mitigate risks effectively. By reading this, you'll gain the expertise to confidently manage LLM security challenges. What this Book will help me do Learn about adversarial AI attacks and methods to defend against them. Understand secure-by-design methodologies and their application to LLM systems. Gain insights on implementing MLSecOps practices for robust AI security. Navigate ethical considerations and legal aspects of AI security. Secure AI development life cycles with practical strategies and standards. Author(s) The authors, Vaibhav Malik, Ken Huang, and Adam Dawson, are experts in AI security with collective experience covering cybersecurity, AI development, and security frameworks. Their dedication to advancing trustworthy AI ensures that this book is both technically comprehensive and approachable. Who is it for? This book is perfect for cybersecurity experts, AI developers, and technology managers aiming to secure and manage AI systems. Readers should have a basic understanding of AI and security concepts. If you're a security architect, ML engineer, DevOps professional, or a leader overseeing AI initiatives, this book will help you address LLM security effectively for your field.

In a world where data sovereignty, scalability, and AI innovation are at the forefront of enterprise strategy, PostgreSQL is emerging as the key to unlocking transformative business value. This new guide serves as your beacon for navigating the convergence of AI, open source technologies, and intelligent data platforms. Authors Tom Taulli, Benjamin Anderson, and Jozef de Vries offer a strategic and practical approach to building AI and data platforms that balance innovation with governance, empowering organizations to take control of their data future. Whether you're designing frameworks for advanced AI applications, modernizing legacy infrastructures, or solving data challenges at scale, you can use this guide to bridge the gap between technical complexity and actionable strategy. Written for IT executives, data leaders, and practitioners alike, it will equip you with the tools and insights to harness Postgre's unique capabilities—extensibility, unstructured data management, and hybrid workloads—for long-term success in an AI-driven world. Learn how to build an AI and data platform using PostgreSQL Overcome data challenges like modernization, integration, and governance Optimize AI performance with model fine-tuning and retrieval-augmented generation (RAG) best practices Discover use cases that align data strategy with business goals Take charge of your data and AI future with this comprehensive and accessible roadmap

Seasoned Experts Alberto Ferrari and Marco Russo Help You Master DAX for Superior Business Intelligence Solutions The Definitive Guide to DAX is an authoritative resource for mastering the DAX language, which is pivotal for creating measures for semantic models in Microsoft Power BI, Fabric, Analysis Services, and Excel. With years of experience since the inception of Power Pivot, the authors offer a comprehensive reference that navigates through the unique and complex concepts of DAX, making it an essential tool for BI professionals. This third edition updates you with the latest features and provides a framework for writing efficient DAX code, enhancing your ability to customize calculations in data models. You will gain the skills necessary to understand and apply advanced DAX concepts, structure code in user-defined functions, and leverage new calendars for time intelligence calculations. By reading this book, you will: Understand the foundational concepts of DAX and its unique language structure Master the use of evaluation contexts to enhance data analysis Utilize CALCULATE and CALCULATETABLE functions effectively Implement variables to simplify complex DAX expressions Leverage classic and new calendar based time intelligence functions Work with iterators to perform advanced calculations Apply visual calculations to improve report interactivity Create and manage calculation groups for dynamic reporting Handle complex hierarchies and relationships in data models Author DAX queries for testing and troubleshooting About This Book For BI professionals and Excel power users eager to deepen their understanding of DAX and enhance their data modeling capabilities with advanced techniques For data analysts and IT specialists seeking to optimize their use of Microsoft Power BI, Fabric, Analysis Services, and Excel for more efficient and insightful data analysis

Transform Your Business with Intelligent AI to Drive Outcomes Building reactive AI applications and chatbots is no longer enough. The competitive advantage belongs to those who can build AI that can respond, reason, plan, and execute. Building Agentic AI: Workflows, Fine-Tuning, Optimization, and Deployment takes you beyond basic chatbots to create fully functional, autonomous agents that automate real workflows, enhance human decision-making, and drive measurable business outcomes across high-impact domains like customer support, finance, and research. Whether you're a developer deploying your first model, a data scientist exploring multi-agent systems and distilled LLMs, or a product manager integrating AI workflows and embedding models, this practical handbook provides tried and tested blueprints for building production-ready systems. Harness the power of reasoning models for applications like computer use, multimodal systems to work with all kinds of data, and fine-tuning techniques to get the most out of AI. Learn to test, monitor, and optimize agentic systems to keep them reliable and cost-effective at enterprise scale. Master the complete agentic AI pipeline Design adaptive AI agents with memory, tool use, and collaborative reasoning capabilities Build robust RAG workflows using embeddings, vector databases, and LangGraph state management Implement comprehensive evaluation frameworks beyond accuracy, including precision, recall, and latency metrics Deploy multimodal AI systems that seamlessly integrate text, vision, audio, and code generation Optimize models for production through fine-tuning, quantization, and speculative decoding techniques Navigate the bleeding edge of reasoning LLMs and computer-use capabilities Balance cost, speed, accuracy, and privacy in real-world deployment scenarios Create hybrid architectures that combine multiple agents for complex enterprise applications Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

This book serves as a comprehensive guide to mastering Microsoft Power Automate, offering step-by-step instructions for creating and managing low-code workflows. From beginner to advanced techniques, it covers cloud and RPA functionalities, enhanced by AI features like Co-pilot. You'll gain the skills to build, analyze, and optimize powerful automations tailored to your organization's needs. What this Book will help me do Understand and implement workflows using Power Automate's connectors and triggers for seamless integration. Utilize AI Builder and the Co-pilot feature to design intelligent workflows with generative AI capabilities. Master robotic process automation to bridge digital and legacy systems effectively. Learn to monitor and troubleshoot workflows while ensuring security and compliance in automation. Scale and govern enterprise-level workflows with best practices for maintainability. Author(s) Aaron Guilmette is a seasoned expert in the field of workflow automation with extensive experience in the Microsoft ecosystem. As the author of multiple books on Power Automate, Aaron combines technical depth with practical know-how. He brings a hands-on approach to guiding readers through advanced features, making automation accessible and effective. Who is it for? This book is ideal for power users, information workers, and citizen developers looking to integrate automation into their work. Whether you're new to automation or expanding your skills, this book provides actionable insights. Familiarity with the Microsoft 365 platform is recommended but not required, as the book covers foundational as well as advanced topics. It is perfect for anyone aiming to streamline workflows and drive efficiency in their projects or organization.

EXPLORE THIS INDISPENSABLE AND COMPREHENSIVE GUIDE TO TIME SERIES ANALYSIS FOR STUDENTS AND PRACTITIONERS IN A WIDE VARIETY OF DISCIPLINES Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference delivers an accessible guide to time series analysis that includes both theory and practice. The coverage spans developments from ARIMA intervention models and generalized least squares to the London School of Economics (LSE) approach and vector autoregression. Designed to break difficult concepts into manageable pieces while offering plenty of examples and exercises, the author demonstrates the use of lag operator algebra throughout to provide a better understanding of dynamic specification and the connections between model specifications that appear to be more different than they are. The book is ideal for those with minimal mathematical experience, intended to follow a course in multiple regression, and includes exercises designed to build general skills such as mathematical expectation calculations to derive means and variances. Readers will also benefit from the inclusion of: A focus on social science applications and a mix of theory and detailed examples provided throughout An accompanying website with data sets and examples in Stata, SAS and R A simplified unit root testing strategy based on recent developments An examination of various uses and interpretations of lagged dependent variables and the common pitfalls students and researchers face in this area An introduction to LSE methodology such as the COMFAC critique, general-to-specific modeling, and the use of forecasting to evaluate and test models Perfect for students and professional researchers in the political sciences, public policy, sociology, and economics, Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference will also earn a place in the libraries of post graduate students and researchers in public health, public administration and policy, and education.

Secure your expertise in the next wave of computing with this essential book, which provides a comprehensive guide to Edge AI, detailing its foundational concepts, deployment strategies, and real-world applications for revolutionizing performance and privacy across various industries. Edge AI has the potential to bring the computational power of AI algorithms closer to where data is generated, processed, and utilized. Traditionally, AI models are deployed in centralized cloud environments, leading to latency issues, bandwidth constraints, and privacy concerns. Edge AI addresses these limitations by enabling AI inference and decision-making directly on edge devices, such as smartphones, IoT sensors, and edge servers. Despite its challenges, edge AI presents numerous opportunities across various domains. From real-time health monitoring and predictive maintenance in industrial IoT to personalized recommendations in retail and immersive experiences in augmented reality, edge AI has the potential to revolutionize how we interact with technology. This book aims to provide a comprehensive exploration of edge AI, covering its foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, emerging trends, and real-world applications. This guide is essential for anyone pushing the boundaries to leverage edge computing for enhanced performance and efficiency. Readers will find this volume: Dives deep into the world of edge AI with a comprehensive exploration covering foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, governance frameworks, optimization techniques, and real-world applications; Offers practical guidance on implementing edge AI solutions effectively in various domains, including architecture design, development frameworks, deployment strategies, and optimization techniques; Explores concrete examples of edge AI applications across diverse domains such as healthcare, industrial IoT, smart cities, and autonomous systems, providing insights into how edge AI is revolutionizing industries and everyday life; Provides insights into emerging trends and technologies in the field of edge AI, including convergence with blockchain, augmented reality, virtual reality, autonomous systems, personalized experiences, and cybersecurity. Audience Researchers, AI experts, and industry professionals in the field of computer science, IT, and business management.

This book is a vital resource for anyone looking to understand the essential role of graph theory as the unifying thread that connects and provides innovative solutions across a wide spectrum of modern computer science disciplines. Graph theory is a traditional mathematical discipline that has evolved as a basic tool for modeling and analyzing the complex relationships between different technological landscapes. Graph theory helps explain the semantic and syntactic relationships in natural language processing, a technology behind many businesses. Disciplinary and industry developments are seeing a major transition towards more interconnected and data-driven decision-making, and the application of graph theory will facilitate this transition. Disciplines such as parallel and distributive computing will gain insights into how graph theory can help with resource optimization and job scheduling, creating considerable change in the design and development of scalable systems. This book provides comprehensive coverage of how graph theory acts as the thread that connects different areas of computer science to create innovative solutions to modern technological problems. Using a multi-faceted approach, the book explores the fundamentals and role of graph theory in molding complex computational processes across a wide spectrum of computer science.

A hands-on resource designed to teach the mathematics of signals and systems with MATLAB™ In this newly revised second edition of Practical Signals Theory with MATLAB™ Applications, Richard Tervo delivers an articulate presentation of the mathematics underlying real world engineering applications and everyday electronic devices. The new edition provides extended coverage of communication systems—including digital and spread spectrum communications—as well as a new introductory chapter on using MATLAB™ as a tool to visualize the mathematics of signals and systems. The text contains numerous hands-on examples and expanded end-of-chapter exercises. It is a one-stop reference for signals and systems, explaining aspects of commonplace signal types, orthogonality and signal decomposition, transformations, and the graphical presentation of calculations and results. Readers will also find: A solid introduction to the mathematics of continuous and discrete signals represented in time and frequency domains Thorough coverage of the classic Fourier, Laplace and z-transforms, and their many applications New end-of-chapter worked exercises, a variety of in-line study questions with answers and easily reproducible MATLAB™ code demonstrations Bonus material on related applications in different fields of study and a companion website designed to support additional learning Perfect for undergraduate and graduate students of signals and systems, signals theory, and related areas of electrical engineering, Practical Signals Theory with MATLAB™ Applications will also benefit researchers and professors in the field of system design and signal processing.

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can't do Learn the basics of machine learning code and how it integrates with large datasets Understand the mathematical principles that AI uses to make itself smarter Consider real-world applications of machine learning and write your own algorithms With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

Deliver measurable business value by applying strategic, technical, and ethical frameworks to AI initiatives at scale Free with your book: DRM-free PDF version + access to Packt's next-gen Reader Key Features Build AI strategies that align with business goals and maximize ROI Implement enterprise-ready frameworks for MLOps, LLMOps, and Responsible AI Learn from real-world case studies spanning industries and AI maturity levels Book Description AI is only as valuable as the business outcomes it enables, and this hands-on guide shows you how to make that happen. Whether you’re a technology leader launching your first AI use case or scaling production systems, you need a clear path from innovation to impact. That means aligning your AI initiatives with enterprise strategy, operational readiness, and responsible practices, and The AI Optimization Playbook gives you the clarity, structure, and insight you need to succeed. Through actionable guidance and real-world examples, you’ll learn how to build high-impact AI strategies, evaluate projects based on ROI, secure executive sponsorship, and transition prototypes into production-grade systems. You’ll also explore MLOps and LLMOps practices that ensure scalability, reliability, and governance across the AI lifecycle. But deployment is just the beginning. This book goes further to address the crucial need for Responsible AI through frameworks, compliance strategies, and transparency techniques. Written by AI experts and industry leaders, this playbook combines technical fluency with strategic perspective to bridge the business–technology divide so you can confidently lead AI transformation across the enterprise. Email sign-up and proof of purchase required What you will learn Design business-aligned AI strategies Select and prioritize AI projects with the highest potential ROI Develop reliable prototypes and scale them using MLOps pipelines Integrate explainability, fairness, and compliance into AI systems Apply LLMOps practices to deploy and maintain generative AI models Build AI agents that support autonomous decision-making at scale Navigate evolving AI regulations with actionable compliance frameworks Build a future-ready, ethically grounded AI organization Who this book is for This book is for AI/ML leaders and business leaders, CTOs, CIOs, CDAOs, and CAIOs, responsible for driving innovation, operational efficiency, and risk mitigation through artificial intelligence. You should have familiarity with enterprise technology and the fundamentals of AI solution development.

Build effective data models and reports in Power BI for financial planning, budgeting, and valuations with practical templates, logic, and step-by-step guidance. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader Key Features Engineer optimal star schema data models for financial planning and analysis Implement common financial logic, calendars, and variance calculations Create dynamic, formatted reports for income statements, balance sheets, and cash flow Purchase of the print or Kindle book includes a free PDF eBook Book Description Martin Kratky brings his global experience of over 20 years as co-founder of Managility and creator of Acterys to empower CFOs and accountants with Power BI for Finance through this hands-on guide to streamlining and enhancing financial processes. Starting with the foundation of every effective BI solution, a well-designed data model, the book shows you how to structure star schemas and integrate common financial data sources like ERP and accounting systems. You’ll then learn to implement key financial logic using DAX and M, covering calendars, KPIs, and variance calculations. The book offers practical advice on creating clear and compliant financial reports, such as income statements, balance sheets, and cash flows with visual design and formatting best practices. With dedicated chapters on advanced workflows, you’ll learn how to handle multi-currency setups, perform group consolidations, and implement planning models like rolling forecasts, annual budgets, and sales and operations planning (S&OP). As you advance, you’ll gain insights from real-world case studies covering company valuations, Excel integration, and the use of write-back methods with Dynamics Business Performance Planning and Acterys. The concluding chapters highlight how AI and Copilot enhance financial analytics. Email sign-up and proof of purchase required What you will learn Apply multi-currency handling and group consolidation techniques in Power BI Model discounted cash flow and company valuation scenarios Design and manage write-back workflows with Dynamics BPP and Acterys Integrate Excel and Power BI using live connections and cube formulas Utilize AI, Copilot, and LLMs to enhance automation and insight generation Create complete finance-focused dashboards for sales and operations planning Who this book is for This book is for finance professionals including CFOs, FP&A managers, controllers, and certified accountants who want to enhance reporting, planning, and forecasting using Power BI. Basic familiarity with Power BI and financial concepts is recommended to get the most out of this hands-on guide.