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Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia's APIs, libraries, and packages. This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents. The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners. What You Will Learn Work with Julia types and the different containers for rapid development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Use C/C++, Python or R libraries in Julia and embed Julia in other code. Optimize performance with GPU programming, profiling and more. Manage, prepare, analyse and visualise your data with DataFrames and Plots Implement complete ML workflows with BetaML, from data coding to model evaluation, and more. Who This Book Is For Experienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.

Foreword by Dr. Shreyas Subramanian, Principal Data Scientist, Amazon This book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOps tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps By the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS. What you will learn: ● Create repeatable training workflows to accelerate model development ● Catalog ML artifacts centrally for model reproducibility and governance ● Integrate ML workflows with CI/CD pipelines for faster time to production ● Continuously monitor data and models in production to maintain quality ● Optimize model deployment for performance and cost Who this book is for: This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.

This book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities. The centerpiece of this book is a risk management and assessment framework titled “Safe Human-centered AI (SAFE-HAI),” which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks. This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission). What You Will Learn Know the key principles behind Responsible AI and associated risks Become familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risks Be aware of the fundamentals of AI regulations and policies and how to adopt them Understand AI governance basics and implementation guidelines Who This Book Is For AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities

Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance. This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models. By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges. What You Will Learn Understand the fundamentals of linear regression and its applications in financial data analysis and prediction Apply generalized linear models for handling various types of data distributions and enhancing model flexibility Gain insights into regime switching models to capture different market conditions and improve financial forecasting Benchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications Who This Book Is For Data scientists, machine learning engineers, finance professionals, and software engineers

Gain practical knowledge of application implementation using various programming approaches in predictive analytics. This book serves as a comprehensive guide for both beginners and professionals in the field of predictive analytics, offering core principles and practical insights without requiring an extensive mathematics or statistics background. The book starts with an introduction to analytics in decision making, protective analytics basics, and implementation in various industries. The book then takes you through types of regression, and simple linear regression in detail, followed by a demonstration of R Studio and SAS. Multiple Linear Regression is discussed next along with MLR model diagnostics. The book covers Multivariate Analysis and teaches you how to work with Principal Components Analysis, Factor Analysis, and much more. You also learn Time series Analysis with an understanding of Autoregressive Moving Average (ARMA) Models. After reading the book, you will be able to put predictive analytics principles into practice. What You Will Learn Understand modeling, estimating, and evaluating models for forecasting Implement Partial F-Test and Variable Selection Method Demonstrate each analysis model in R Studio and SAS Understand SLR and MLR Analysis models Who This Book Is For Students and professionals in the field of data analysis and intelligence applications

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

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. What You Will Learn Different approaches to AI adoption, from building in-house AI capabilities to partnering with external providers Key factors to consider when choosing an AI solution and how to ensure its successful integration into existing workflows AI technologies, their business impact, and ethical considerations to make informed decisions and foster responsible AI The environmental impacts of AI systems and the need for sustainable practices in AI development and deployment. Who This Book is For Business executives and process owners/representatives, risk officers, cybersecurity professionals, legal counsel and ethics officers, human resource professionals, data scientists, AI developers, and CTOs.

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? Who This Book Is For Machine learning engineers, data scientists, AI practitioners, software developers, and engineers interested in deep learning

Learn the United Nations Sustainable Development Goals (UN SDGs) and see how machine learning can significantly contribute to their realization. This book imparts both theoretical knowledge and hands-on experience in comprehending and constructing machine learning-based applications for addressing multiple UN SDGs using JavaScript. The reading begins with a delineation of diverse UN SDG targets, providing an overview of previous successful applications of machine learning in solving realistic problems aligned with these targets. It thoroughly explains fundamental concepts of machine learning algorithms for prediction and classification, coupled with their implementation in JavaScript and HTML programming. Detailed case studies examine challenges related to renewable energy, agriculture, food production, health, environment, climate change, water quality, air quality, and telecommunications, corresponding to various UN SDGs. Each case study includes related works, datasets, machine learning algorithms, programming concepts, and comprehensive explanations of JavaScript and HTML codes used for web-based machine learning applications. The results obtained are meticulously analyzed and discussed, showcasing the pivotal role of machine learning in advancing the relevant SDGs. By the end of this book, you'll have a firm understanding of SDG fundamentals and the practical application of machine learning to address diverse challenges associated with these goals. What You'll Learn Understand the fundamental concepts of the UN SDGs, AI, and machine learning algorithms. Employ the correct machine learning algorithms to address challenges on the United Nations Sustainable Development Goals (UN SDGs)? Develop web-based machine learning applications for the UN SDGs using Javascript, and HTML. Analyze the impact of a machine learning-based solution on a specific UN SDG. Who This Book Is For Data scientists, machine learning engineers, software professionals, researchers, and graduate students.

Gain the knowledge and practical skills needed to deploy a fully functional, secure, and scalable application in AWS EKS. This book is a hands-on guide designed to help you navigate and manage infrastructure in the AWS EKS environment. The book starts by reviewing the concept of DevOps flexibility, emphasizing adaptability to manage changes effectively. It covers foundational elements like IAM basics, user management, and the use of Terraform for infrastructure as code, highlighting the importance of version stability and unique CIDR blocks. Moving forward, you'll explore Kubernetes, and its initial set-up steps, such as configuration management, access control, and storage. You'll also see how to bridge Kubernetes with AWS resources, touching upon service accounts, identity federation, and policy assignment. In the final chapters, the book guides you through the complete setup of a fully working application using Terraform. The book culminates with a step-by-step walkthrough of deploying a complete application environment where you'll generate and distribute access credentials, configure persistent storage, and manage RBAC for users, all using Terraform. Zero-Maintenance Strategies for AWS EKS will provide you with a solid foundation and a toolkit of proven strategies for managing cloud infrastructure. What You Will Learn Understand the concept of DevOps flexibility and its practical applications Acquire foundational knowledge in IAM basics and user management. Manage external access to your applications while consolidating load balancing through a single, entry point. Securely configure DNS and SSL, ensuring your application is both accessible and protected. Who This Book Is For DevOps professionals, IT managers, and software developers who are looking to adopt or improve their DevOps practices, and students and educators in IT-related fields.

This is your guide book to Generative AI (GenAI) and its application in addressing real-world challenges within the public sector. The book addresses a range of topics from GenAI concepts and strategy to public sector use cases, architecture patterns, and implementation best practices. With a general background in technology and the public sector, you will be able to understand the concepts in this book. The book will help you develop a deeper understanding of GenAI and learn how GenAI differs from traditional AI. You will explore best practices such as prompt engineering, and fine-tuning, and architectural patterns such as Retrieval Augmented Generation (RAG). And you will discover specific nuances, considerations, and strategies for implementation in a public sector organization. You will understand how to apply these concepts in a public sector setting and address industry-specific challenges and problems by studying a variety of use cases included in the book in the areas of content generation, chatbots, summarization, and program management. What You Will Learn GenAI concepts and how GenAI differs from traditional AI/ML Prompt engineering, fine-tuning, RAG, and customizing foundation models Strategy, methodologies, and frameworks for the public sector Public sector use cases in the areas of content generation, summarization, and chatbots, plus program management, analytics, business intelligence, and reporting Architecture and design patterns Implementation, operations, and maintenance of GenAI applications Who This Book Is For Technology and business leaders in the public sector who are new to AI/ML and are keen on exploring and harnessing the potential of Generative AI in their respective organizations.

Understand and apply the design patterns outlined in this book to design, develop, and deploy scalable AI solutions that meet your organization's needs and drive innovation in the era of intelligent automation. This book begins with an overview of scalable AI systems and the importance of design patterns in creating robust intelligent solutions. It covers fundamental concepts and techniques for achieving scalability in AI systems, including data engineering practices and strategies. The book also addresses scalable algorithms, models, infrastructure, and architecture considerations. Additionally, it discusses deployment, productionization, real-time and streaming data, edge computing, governance, and ethics in scalable AI. Real-world case studies and best practices are presented, along with insights into future trends and emerging technologies. The book focuses on scalable AI and design patterns, providing an understanding of the challenges involved in developing AI systems that can handle large amounts of data, complex algorithms, and real-time processing. By exploring scalability, you will be empowered to design and implement AI solutions that can adapt to changing data requirements. What You Will Learn Develop scalable AI systems that can handle large volumes of data, complex algorithms, and real-time processing Know the significance of design patterns in creating robust intelligent solutions Understand scalable algorithms and models to handle extensive data and computing requirements and build scalable AI systems Be aware of the ethical implications of scalable AI systems Who This Book Is For AI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution.This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.What You Will LearnWrite code for machine learning, adaptive control, and estimation using MATLABUse MATLAB graphics and visualization tools for machine learningBecome familiar with neural netsBuild expert systemsUnderstand adaptive controlGain knowledge of Kalman FiltersWho This Book Is ForSoftware engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists.

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 Who This Book Is For Data scientists looking to expand their skill set to include deep learning on AWS, machine learning engineers tasked with designing and deploying machine learning systems who want to incorporate deep learning capabilities into their applications, AI practitioners working across various industries who seek to leverage deep learning for solving complex problems and gaining a competitive advantage

Explore Snowflake's core concepts and unique features that differentiates it from industry competitors, such as, Azure Synapse and Google BigQuery. This book provides recipes for architecting and developing modern data pipelines on the Snowflake data platform by employing progressive techniques, agile practices, and repeatable strategies. You'll walk through step-by-step instructions on ready-to-use recipes covering a wide range of the latest development topics. Then build scalable development pipelines and solve specific scenarios common to all modern data platforms, such as, data masking, object tagging, data monetization, and security best practices. Throughout the book you'll work with code samples for Amazon Web Services, Microsoft Azure, and Google Cloud Platform. There's also a chapter devoted to solving machine learning problems with Snowflake. Authors Dillon Dayton and John Eipe are both Snowflake SnowPro Core certified, specializing in data and digital services, and understand the challenges of finding the right solution to complex problems. The recipes in this book are based on real world use cases and examples designed to help you provide quality, performant, and secured data to solve business initiatives. What You'll Learn Handle structured and un- structured data in Snowflake. Apply best practices and different options for data transformation. Understand data application development. Implement data sharing, data governance and security. Who This book Is For Data engineers, scientists and analysts moving into Snowflake, looking to build data apps. This book expects basic knowledge in Cloud (AWS or Azure or GCP), SQL and Python

This comprehensive guide, featuring hand-picked examples of daily use cases, will walk you through the end-to-end predictive model-building cycle using the latest techniques and industry tricks. In Chapters 1, 2, and 3, we will begin by setting up the environment and covering the basics of PySpark, focusing on data manipulation. Chapter 4 delves into the art of variable selection, demonstrating various techniques available in PySpark. In Chapters 5, 6, and 7, we explore machine learning algorithms, their implementations, and fine-tuning techniques. Chapters 8 and 9 will guide you through machine learning pipelines and various methods to operationalize and serve models using Docker/API. Chapter 10 will demonstrate how to unlock the power of predictive models to create a meaningful impact on your business. Chapter 11 introduces some of the most widely used and powerful modeling frameworks to unlock real value from data. In this new edition, you will learn predictive modeling frameworks that can quantify customer lifetime values and estimate the return on your predictive modeling investments. This edition also includes methods to measure engagement and identify actionable populations for effective churn treatments. Additionally, a dedicated chapter on experimentation design has been added, covering steps to efficiently design, conduct, test, and measure the results of your models. All code examples have been updated to reflect the latest stable version of Spark. You will: Gain an overview of end-to-end predictive model building Understand multiple variable selection techniques and their implementations Learn how to operationalize models Perform data science experiments and learn useful tips

This comprehensive guide provides practical strategies for optimizing Generative AI systems to be more sustainable and responsible. As advances in Generative AI such as large language models accelerate, optimizing these resource-intensive workloads for efficiency and alignment with human values grows increasingly urgent. The book starts with the concept of Generative AI and its wide-ranging applications, while also delving into the environmental impact of AI workloads and the growing importance of adopting sustainable AI practices. It then delves into the fundamentals of efficient AI workload management, providing insights into understanding AI workload characteristics, measuring performance, and identifying bottlenecks and inefficiencies. Hardware optimization strategies are explored in detail, covering the selection of energy-efficient hardware, leveraging specialized AI accelerators, and optimizing hardware utilization and scheduling for sustainable operations. You are also guided through software optimization techniques tailored for Generative AI, including efficient model architecture, compression, and quantization methods, and optimization of software libraries and frameworks. Data management and preprocessing strategies are also addressed, emphasizing efficient data storage, cleaning, preprocessing, and augmentation techniques to enhance sustainability throughout the data life cycle. The book further explores model training and inference optimization, cloud and edge computing strategies for Generative AI, energy-efficient deployment and scaling techniques, and sustainable AI life cycle management practices, and concludes with real-world case studies and best practices By the end of this book, you will take away a toolkit of impactful steps you can implement to minimize the environmental harms and ethical risks of Generative AI. For organizations deploying any type of generative model at scale, this essential guide provides a blueprint for developing responsible AI systems that benefit society. What You Will Learn Understand how Generative AI can be more energy-efficient through improvements such as model compression, efficient architecture, hardware optimization, and carbon footprint tracking Know the techniques to minimize data usage, including evaluation, filtering, synthesis, few-shot learning, and monitoring data demands over time Understand spanning efficiency, data minimization, and alignment for comprehensive responsibility Know the methods for detecting, understanding, and mitigating algorithmic biases, ensuring diversity in data collection, and monitoring model fairness Who This book Is For Professionals seeking to adopt responsible and sustainable practices in their Generative AI work; leaders and practitioners who need actionable strategies and recommendations that can be implemented directly in real-world systems and organizational workflows; ML engineers and data scientists building and deploying Generative AI systems in industry settings; and researchers developing new generative AI techniques, such as at technology companies or universities

This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing. What You Will Learn Gain practical experience by working with models from OpenAI and the Hugging Face library Use essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databases Create and implement projects using LLM while understanding the design decisions involved Understand the role of Large Language Models in larger corporate settings Who This Book Is For Data analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks

The world stands at a pivotal moment due to the emergence of Generative AI, specifically ChatGPT. This groundbreaking technology has provoked and impressed almost every industry globally, evoking every emotion from awe to anxiety. Many are apprehensive about the future, fearing job losses due to rapid artificial intelligence (AI advancements). But if history has taught us anything, progress, while challenging, often paves the way for broader opportunities and growth. This book explains in depth the core building blocks that make up the current landscape of transformer and language models and, more broadly speaking, AI as a whole. We have seen how the internet and the mobile revolution changed our world. Is AI following a similar trajectory? Are we on the verge of something even more transformative? This book strives to provide a complete picture of the challenges and opportunities and the implications for our shared futures. In subsequent chapters, the book will discuss language models in depth. These are not just algorithms; they represent a nexus of linguistics, cognitive science, and cutting-edge technology. You'll trace AI's unexpected and exhilarating evolution, observing how it has grown from a mere concept to a force reshaping entire industries. Finally, you'll consider the rise of AI in the context of advancements. While ChatGPT has gained significant attention for certain applications, it's essential to recognize that its capabilities extend far beyond what's immediately evident. Artificial Intelligence, represented by models like ChatGPT, is not a static field. It's dynamic and ever-evolving, and its potential applications are broadening each day. Technology is not, by any means, limited to chatbots or translation use cases. This book captures this vast and ever-expanding horizon of possibilities. What You Will Learn How large language models came to be, and how they work What ethical AI design looks like The role of regulation in artificial intelligence Why you should not be afraid of losing your job Who This Book is For Working professionals and students in any field

Gain a theoretical understanding to the most popular libraries in deep reinforcement learning (deep RL). This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments ranging from games, and robotics to finance are explained to help you try different ways to apply reinforcement learning. A chapter on multi-agent reinforcement learning covers how multiple agents compete, while another chapter focuses on the widely used deep RL algorithm, proximal policy optimization (PPO). You'll see how reinforcement learning with human feedback (RLHF) has been used by chatbots, built using Large Language Models, e.g. ChatGPT to improve conversational capabilities.You'll also review the steps for using the code on multiple cloud systems and deploying models on platforms such as Hugging Face Hub. The code is in Jupyter Notebook, which canbe run on Google Colab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs. Whether it's for applications in gaming, robotics, or Generative AI, Deep Reinforcement Learning with Python will help keep you ahead of the curve.What You'll LearnExplore Python-based RL libraries, including StableBaselines3 and CleanRL Work with diverse RL environments like Gymnasium, Pybullet, and Unity MLUnderstand instruction finetuning of Large Language Models using RLHF and PPOStudy training and optimization techniques using HuggingFace, Weights and Biases, and Optuna Who This Book Is ForSoftware engineers and machine learning developers eager to sharpen their understanding of deep RL and acquire practical skills in implementing RL algorithms fromscratch.

Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness. The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack. This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps. What You'll Learn Gain an understanding of the MLOps discipline Know the MLOps technical stack and its components Get familiar with the MLOps adoption strategy Understand feature engineering Who This Book Is For Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production

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. Who This Book is for: The primary audience is cybersecurity professionals looking to improve their organization's security posture by leveraging AI and ML-based security tools and combining them into a comprehensive strategy incorporating automation and ACD. This target audience will have a cybersecurity background and an interest in AI and ML. Higher education would be a secondary audience.

As leaders plan to make significant investments to harness the power of foundational models such as ChatGPT, they need to understand the changes in organizational behaviors required for the successful implementation of such systems. The size, complexity and nature of this new wave of technologies requires a refresh in roles and responsibilities in conventional IT organizations. This book reveals practical and no-nonsense guidance on how to leverage generative AI to transform your business processes and organizational structures to achieve breakthroughs in efficiency, effectiveness and competitive advantage. Written in a lively, engaging, and often humorous style, this work provides practical insights and timely survival skills for leaders with anonymous but real-world experiences and case studies. If you're looking to understand how large language foundation models such as ChatGPT are reshaping managerial roles and organizational structures, and how they can leverage this knowledge to survive and thrive in this brave new world then Organizing for Generative AI and the Productivity Revolution is the book for you. What You Will Learn Review the key changes in current state roles and responsibilities that are required to successfully deploy generative AI systems Examine the organizational reporting structures and associated incentives that form a strong generative AI system Understand the financial, regulatory, and operational risks created by organizational behavioral issues that arise when organizations build and deploy large language models Compare the strategic differences in emerging versus traditional organizational behaviors, incentives, roles and responsibilities Who This Book Is For Executives and team leaders at enterprises large and small. “The book addresses an important topic: how to set up an organization for success, particularly in Generative AI. Generative AI brings new challenges to organizations in terms of how to structure the organization for success, mitigating risks, software development lifecycle, and tracking ROI. I could think of no better person to tackle these issues than Arthur O'Connor, who has extensive experience in technology within some of the largest enterprises in Wall Street, complemented by his academic background. He brings an insightful and unique perspective for technology leaders who want to set up their organizations for success in Generative AI.” - Joseph Sabelja, Generative AI Firmwide Platform Lead, J P Morgan

This is a comprehensive exploration into the world of Artificial Intelligence, designed to bridge the gap between theoretical concepts and practical, real-world applications. This book unravels the mystique of AI, breaking down its components into understandable elements. From the early dawn of AI's inception to its current state of rapid evolution, we cover the essential building blocks necessary for leveraging AI in business, and personal development, and understanding its broader impacts on society. Through an engaging conversational format, readers are guided through the intricacies of AI, covering topics such as machine learning, AI governance,, data security, and the ethical challenges facing AI today. This book is an invaluable resource for those looking to understand the fundamentals of AI, its practical applications, and its significant implications for the future. After reading this book, you will be able to integrate AI into your business strategies and learn the intricacies of AI advancements. What You Will Learn: Key concepts and definitions within AI, including types of AI, machine learning, and neural networks and how they are utilized in AI apps like M365 Copilot Practical applications of AI for personal and business growth, focusing on the pillars of using AI to evolve these fronts effectively and sustainably How AI is transforming businesses and what organizational shifts must be made to realize the value Navigating the challenges and ethical considerations in AI to ensure informed and responsible usage Who This Book Is For: Professionals looking to integrate AI into their business strategies or organizations.

This book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models. The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today's AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation. By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI. What You Will Learn What is Generative Artificial Intelligence? What are text-to-image synthesis techniques and conditional image generation? What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques? What are text-to-video models and how do you tune them? What are large language models, and how do you tune them? Who This Book Is For Those with intermediate to advanced technical knowledge in artificial intelligence and machine learning

This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various devices and web browsers. The book begins by delving into NTMA, explaining fundamental concepts and providing an overview of existing applications and research within this domain. It also goes into the essentials of VQA and offers a survey of the latest developments in VQA algorithms. The book includes a thorough examination of machine learning algorithms that find application in both NTMA and VQA, with a specific emphasis on classification and prediction algorithms such as the Multi-Layer Perceptron and Support Vector Machine. The book also explores the software architecture of the NTMA client-server application. This architecture is meticulously developed using HTML, CSS, Node.js, and JavaScript. Practical aspects of developing the Video Quality Assessment (VQA) model using JavaScript and Java are presented. Lastly, the book provides detailed guidance on implementing a complete system model that seamlessly merges NTMA and VQA into a unified web application, all built upon a client-server paradigm. By the end of the book, you will understand NTMA and VQA concepts and will be able to apply machine learning to both domains and develop and deploy your own NTMA and VQA applications using JavaScript and Node.js. What You Will Learn What are the fundamental concepts, existing applications, and research on NTMA? What are the existing software and current research trends in VQA? Which machine learning algorithms are used in NTMA and VQA? How do you develop NTMA and VQA web-based applications using JavaScript, HTML, and Node.js? Who This Book Is For Software professionals and machine learning engineers involved in the fields of networking and telecommunications

This guide covers the fundamental technical principles and various business applications of Generative AI for planning, developing, and evaluating AI-driven products. It equips you with the knowledge you need to harness the potential of Generative AI for enhancing business creativity and productivity.The book is organized into three sections: text-based, senses-based, and rationale-based. Each section provides an in-depth exploration of the specific methods and applications of Generative AI. In the text-based section, you will find detailed discussions on designing algorithms to automate and enhance written communication, including insights into the technical aspects of transformer-based Natural Language Processing (NLP) and chatbot architecture, such as GPT-4, Claude 2, Google Bard, and others. The senses-based section offers a glimpse into the algorithms and data structures that underpin visual, auditory, and multisensory experiences, including NeRF, 3D Gaussian Splatting,Stable Diffusion, AR and VR technologies, and more. The rationale-based section illuminates the decision-making capabilities of AI, with a focus on machine learning and data analytics techniques that empower applications such as simulation models, agents, and autonomous systems.In summary, this book serves as a guide for those seeking to navigate the dynamic landscape of Generative AI. Whether you're a seasoned AI professional or a business leader looking to harness the power of creative automation, these pages offer a roadmap to leverage Generative AI for your organization's success.What You Will LearnWhat are the technical elements that constitute the makeup of Generative AI products?What are the practical applications of Generative AI?How can algorithms be designed to automate and improve written communication?What are the latest Generative AI architectures and algorithms?Who This Book Is ForData scientists, data analysts, decision makers, and business executives interested in gaining an understanding of Generative AI products

Generative AI has gone beyond the responsibility of researchers and data scientists and is being used by production engineers. However, there is a lot of confusion where to get started when building an end-to-end app with generative AI. This book consolidates core models, frameworks, and tools into a single source of knowledge. By providing hands-on examples, the book takes you through the generative AI ecosystem to build applications for production.The book starts with a brief and accessible introduction to transformer models before delving into some of the most popular large language models and diffusions models (image generation). These models are the foundations of both AI and your potential new apps. You will then go through various tools available to work with these models, starting with Langchain, a framework to develop foundational models, which is the next building block you should grasp after understanding generative AI models. The next chapters cover databases, caching, monitoring, etc., which are the topics necessary to build larger-scale applications. Real-world examples using these models and tools are included.By the end of this book, you should be able to build end-to-end apps that are powered by generative AI. You also should be able to apply the tools and techniques taught in this book to your use cases and business.What You Will LearnWhat is Generative AI?What is ChatGPT and GPT4?What are language models and diffusions models?How do we deploy LangChain and HuggingFace?Who This Book Is ForSoftware engineers with a few years of experience building applications in any language or infrastructure

This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior. The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you'll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You'll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performancedegradation. This includes tracing, logging, and analyzing memory dumps using native WinDbg and GDB debuggers. Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications. What You Will Learn Employ a pattern-oriented approach to Python debugging that starts with diagnostics of common software problemsUse tips and tricks to get the most out of popular IDEs, notebooks, and command-line Python debuggingUnderstand Python internals for interfacing with operating systems and external modulesPerform Python memory dump analysis, tracing, and logging Who This Book Is For Software developers, AI/ML engineers, researchers, data engineers, as well as MLOps and DevOps professionals.

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 Who This Book Is For Data scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques

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, what we learn, what we need to learn, when and where we learn about knowledge production, how humans communicate with each other, the economic, social, political, creative, ethical and technological factors that inform how we navigate human influenced existence on this planet. The technology empowers you to reimagine and reinvent 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. Regenerating Learning will help guide the small team you are a part of, or influence leadership to leverage 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 need to decide if we are going to use the technology and how. What You will Learn • Methods and techniques to re-learn how you learn through your interactions with different generative AI. • Strategic approaches to integrate generative AI within your workflows. • How to iterate, adapt, prototype and learn continuously with generative AI. • A variety of tools and approaches to reconcile your organization's use of generative AI. • How to develop a road map towards the integration of AI systems within your organization. Who this Book Is For Creatives, team leaders, managers and leadership in different organizations; teams in collaborative and creative industries; managers and employees in organizational learning

This transformative book explores the power of artificial intelligence (AI) in revolutionizing the utilities industry. It covers crucial topics such as intelligent grids, decentralized energy resources, customer engagement, electric vehicle integration, and more, providing a comprehensive and practical guide to successfully navigate the energy transition.In today's world, the urgency of addressing climate change and transitioning to sustainable energy systems is undeniable. With approximately 60 percent of global greenhouse gas emissions attributed to the energy sector, utilities play a vital role in achieving sustainability goals. The traditional utility business model faces disruption from renewable energy, changing consumer expectations, and regulatory shifts. Embracing AI emerges as a key solution to optimize operations, enhance grid reliability, and meet evolving customer demands.Through compelling case studies and industry-specific use cases, you will discover how AI drives innovation, improves operational efficiency, and contributes to a greener and more sustainable world. As the demand for cleaner and more sustainable energy practices grows, this book demonstrates how AI can support utilities in meeting these demands, making them more resilient, agile, and customer-centric.Whether you're a seasoned industry expert or a curious student, this book equips you with the knowledge and insights to embrace sustainability, navigate the complex energy landscape, leverage AI to shape a positive future, and join the movement towards a greener world, empowered by AI's potential in the utilities industry.What You Will LearnUnderstand the challenges and opportunities for utilities in the context of climate change, energy poverty, and the evolving business landscapeDiscover how rapid transformation is needed in the utilities sector to overcome challenges and leverage opportunities for a sustainable futureGain insight into the role of technology, particularly artificial intelligence (AI), as a critical tool for utilities in their transformation journeyBe aware of how AI can be applied in building the future utility industry, including its potential impact on energy efficiency, intelligent energy ecosystems, community engagement, and new business modelsGain knowledge of the adoption of AI and machine learning technologies in the utility industry, including the current state, barriers, significant influencing factors, and an AI adoption maturity model for utilitiesRecognize the sustainability imperative for utilities and how AI can help in achieving sustainable energy practicesBecome familiar with the transformation of power generation, microgrids, intelligent transmission and distribution systems, utilities retail, mobility through electric vehicles, and the integration of distributed energy resources(DER) using AIGain insight into the potential of AI in addressing challenges and driving innovation in the energy ecosystem, such as optimizing power generation assets, enhancing grid intelligence, improving customer service, and enabling clean energy awareness in the metaverseWho This Book Is ForProfessionals and decision makers in the global utilities industry who want to leverage artificial intelligence (AI) technologies to transform their operations and address challenges and opportunities in the energy sector. This book may also appeal to researchers, academics, and students in the fields of energy engineering, environmental science, data analytics, and AI who want to gain a deeper understanding of AI in the utilities sector and its implications for sustainable energy systems.

Develop and implement AI strategies aligned with business goals, including operating models and partnership strategies. This book is practical guide for chief experience officers and other corporate board members faced with the complex issues of AI governance, data privacy, AI regulations, AI copyright, AI strategy, and more. Executives are eager to hear from other executives, peers, and authority figures regarding AI matters, and how to approach them quickly, correctly and meaningfully. The cost of missing out or messing up in AI transformation is easy so it's imperative that C-suite and board members have the right mental framework to ask the right questions for their organizations. Throughout this book, you'll see how to develop and execute AI strategies that align with your organizational goals and ethical standards. You'll navigate the complex landscape of AI regulation and governance, applying best practices to ensure compliance and protect stakeholder interests. You'll also, understand how to innovate and adapt AI technologies within your operations. AI and the Boardroom provides all the right tools to guide decision-making, foster partnerships, and enhance customer experiences What You Will Learn Master AI governance, regulations, and ethical considerations, including privacy and intellectual property issues. Optimize AI investments, budgets, and ROI through effective KPIs, OKRs, and risk management. Navigate organizational changes brought by AI, including executive compensation, team structures, and change management. Leverage AI for board-level decision-making while advancing organizational AI maturity and staying ahead of emerging trends. Who This Book Is For Suite executives and corporate board members; technology and innovation leaders; risk management and compliance professionals; corporate strategists and business unit leaders; AI program managers and data scientists

Explore the world of Generative AI and understand why it matters. This book is divided into two parts, introducing tools such as ChatGPT, DALL-E, and will show you how to use them to build AI-powered web apps. The first part of the book describes Generative AI and covers the essential models and APIs from OpenAI. Legal, ethical, and security considerations are discussed to help you decide whether it is an appropriate tool for your projects. You'll then review ChatGPT and see how to use it effectively for generating code. This is followed by a review of best practices, and tips and techniques for getting around the limitations of ChatGPT and other OpenAI APIs. The second part of the book provides practical guide to building a series of web apps with Next.js that showcase how to use the OpenAI APIs. For example, you'll learn how to build a Story/Poetry generator, a language learning app, and a blog site with a custom Chatbot widget. The code for the web apps is generated using ChatGPT. When done with this book, you'll have a clear understanding of Generative AI and be well on your way to building web applications powered by OpenAI APIs and Next.js. What You Will Learn Assess the legal, ethical, and security concerns with using Generative AI in web applications Review the latest APIs provided by OpenAI for generating text and image Use ChatGPT to generate code for web projects, as well as tips and tricks to working around the limitations. Who This Book Is For Experienced web developers and software engineers who know their way around HTML, CSS, and JavaScript, but have limited or no experience using Generative AI to build web applications.

As artificial intelligence (AI) rapidly transforms education, tools like ChatGPT and Claude are revolutionizing the way we teach and learn. This book is a groundbreaking book that empowers parents and students to navigate this exciting new frontier, filling a critical gap in the current literature. As the first comprehensive guide to generative AI in education designed for parents and students, Generative AI in Education is positioned to become an indispensable resource. It provides the knowledge and strategies needed to effectively integrate AI into their learning journeys, transforming educational outcomes and preparing students for success in a rapidly changing world. You'll gain a deep understanding of how tools like ChatGPT and Claude work, and how they can be leveraged to support learning across various subjects and grade levels. You'll then see how to create clear, specific, and engaging prompts that elicit valuable responses from AI-powered tools. This book contains all the techniques for tailoring prompts to different learning objectives, styles, and contexts, and how they can use AI tools to support reading comprehension, writing skills, problem-solving, and creative thinking. What You Will Learn Apply generative AI in education Craft effective prompts for personalized learning experiences Utilize AI tools to support learning, creativity, and problem-solving Who This Book is For Parents and students who are eager to harness the power of generative AI to enhance learning experiences and prepare for success in an AI-driven future

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 knowledge to navigate and leverage transformative potential of Healthcare 4.0 technologies effectively. You Will 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 Learn about emerging trends and future perspectives in Healthcare 4.0, including the potential challenges and recommendations Discover case studies and practical examples illustrating the transformative impact of AI and IoT on patient care Who Is This Book For Readers with foundational understanding of healthcare systems and technologies will benefit most from this book. Specifically, a basic knowledge of healthcare operations, medical terminology, and information technology would be advantageous. Familiarity with concepts related to AI and IoT in healthcare, though not mandatory, would also enhance comprehension of the advanced topics covered in the book.

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. What You Will Learn 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. Who This Book Is For Python programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.

This book explores how to define, design, and maintain enterprise AI applications, exploring the impacts they will have on the teams who work with them. The book is structured into four parts. In Part 1: Defining Your AI Application, you are introduced to the dynamic interplay between human adaptability and AI specialization, the concept of meta systems, and the mechanics of prediction machines. In Part 2: Designing Your AI Application, the book delves into the anatomy of an AI application, unraveling the intricate relationships among data, machine learning, and reasoners. This section introduces the building blocks and enterprise architectural framework for designing multi-agent systems. Part 3: Maintaining Your AI Application takes a closer look at the ongoing life cycle of AI systems. You are guided through the crucial aspects of testing and test automation, providing a solid foundation for effective development practices. This section covers the critical tasks of security and information curation that ensure the long-term success of enterprise AI applications. The concluding section, Part 4: AI Enabled Teams, navigates the evolving landscape of collaborative efforts between humans and AI. It explores the impact of AI on remote work dynamics and introduces the new roles of the expert persona and the AI handler. This section concludes with a deep dive into the legal and ethical dimensions that AI-enabled teams must navigate. This book is a comprehensive guide that not only equips developers, architects, and product owners with the technical know-how of AI application development, but also delves into the broader implications for teams and society. What You Will Learn Understand the algorithms and processes that enable AI to make accurate predictions and enhance decision making Grasp the concept of metasystems and their role in the design phase of AI applications Know how data, machine learning, and reasoners drive the functionality and decision-making capabilities of AI applications Know the architectural components necessary for scalable and maintainable multi-agent AI applications Understand methodologies for testing AI applications, ensuring their robustness, accuracy, and reliability in real-world applications Understand the evolving dynamics of human-AI coordination facing teams in the new enterprise working environment Who This book Is For A diverse audience, primarily targeting enterprise architects, middle managers, tech leads, and team leads entrenched in the IT sector or possessing a tech-savvy background, including professionals such as digital marketers. Additionally, tech-savvy individual contributors—ranging from digital content creators and data analysts to administrators and programmers—stand to benefit significantly.

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 Who This Book Is For Data professionals, including data scientists, data engineers, AI developers, or data governance specialists, as well as managers or decision makers looking to implement or improve data governance practices for machine learning projects

This book helps you explore the fusion of artificial intelligence with the world of online retail, providing an in-depth understanding of how AI technologies are revolutionizing ecommerce. From decoding ecommerce business models to optimizing efficiency in ecommerce deliveries, each chapter delves into a specific aspect of AI-powered ecommerce, providing practical insights and strategies for success. The journey commences with decoding ecommerce business models in a diverse landscape. From direct selling to marketplace platforms, the book illuminates the inner workings of each model, exploring revenue drivers, cost considerations, and the components of profitability. As you progress through the book, it delves deeper into the ecommerce platform itself, exploring its role in facilitating transactions and nurturing customer relationships. Subsequent chapters delve into the intricacies of merchandising in ecommerce, search, recommendation engines, ranking algorithms, personalization techniques, and efficiency improvements. From teaching the science behind matching queries to products to crafting personalized customer experiences, each chapter offers invaluable insights and actionable strategies for leveraging AI in ecommerce. Whether you're a seasoned ecommerce professional or a newcomer, this book equips you with the knowledge and actionable use cases needed to stay ahead of the curve in an increasingly competitive landscape. What You Will Learn Understand the buyer and seller aspects of ecommerce platforms and how they engage customers Know the diverse landscape of ecommerce business models Apply AI/ML technologies to ecommerce to enhance user experience and drive business outcomes Know how AI/ML can influence revenue generation and cost optimization in ecommerce businesses Who This Book Is For Data scientists, machine learning engineers, product and category managers in ecommerce, and business executives

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.

Explore the capabilities of ChatGPT and gain insight on how to utilize this AI tool in your daily tasks, and marketing endeavors. This book introduces ChatGPT, covering its architecture, training process, and applications across various fields. Start by delving into the benefits of integrating ChatGPT into everyday routines, emphasizing its potential to streamline tasks, optimize time management, and provide valuable insights that can revolutionize individuals'work approaches. You'll then look more closely at ChatGPT's mechanics, its capabilities, limitations, and unique features. The book also outlines the best practices for utilizing ChatGPT, offering practical tips, techniques, and strategies to enhance output quality and reliability, while minimizing errors and maximizing results. You'll focus on ChatGPT's relevance in marketing tasks, such as generating product descriptions, creating email templates, automating social media posts, and addressing customer inquiries. The book concludes by exploring techniques for marketing with ChatGPT, including integration with other tools, data analysis, reporting, and customizing ChatGPT to meet specific marketing needs. In the end, you'll have the knowledge and skills needed to leverage ChatGPT's AI marketing capabilities and to harness its power for success in the digital age. What You'll Learn Understand the concepts and workings of ChatGPT, its architecture, and the training process Apply the best practices for ChatGPT Create email templates and automate social media posts using ChatGPT Use ChatGPT for data analysis and reporting Who This Book Is For Marketing professionals, business owners and entrepreneurs, content creators, and customer service representatives

Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more. Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning. What You'll Learn Work with vectors and matrices using NumPy Review Symbolic computing with SymPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Understand statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.

This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code. The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows. What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world. What You Will Learn Elevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speeds Design data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projects Leverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure Who This Book Is For Data analysts, data engineers, data scientists, machine learning engineers, and MLOps specialists

Examine the synergistic possibilities of combining blockchain, IoT, and AI technologies in supply chain management. This book will address the difficulties and possibilities of integrating these technologies and offer helpful implementation advice. The current state of supply chain management involves several challenges, including lack of transparency, limited visibility into product movements, inefficient inventory management, and difficulty in tracking and tracing products. Blockchain, IoT, and AI can potentially address some of these challenges and improve supply chain management. To help illustrate this, case studies and actual instances of businesses implementing or experimenting with blockchain, IoT, and AI technology in their supply chains are included in the book. You'll review helpful advice on implementation and highlight successful deployments, lessons learned, and the impact of these technologies on supply chain visibility, agility, sustainability, and customer experience. This book further elaborates the fundamental concepts of AI and ML algorithms and demonstrates how AI can process enormous volumes of supply chain data to produce actionable insights, improve inventory control, forecast demand, and increase forecasting precision. This collective work will serve as a comprehensive guide for supply chain professionals, researchers, and technology enthusiasts interested in understanding the potential of blockchain, IoT, and AI technologies in revolutionizing supply chain management practices. What You Will Learn Discuss the evolving landscape of technology, potential advancements, and how Blockchain, IoT, and AI can shape the future of supply chainsGain Insight into upcoming developments and prepare for changing dynamics of the industryUnderstand intricate details of blockchain, IoT, and AI Technologies in real-life scenarios Who Is This Book For Industry Professionals working in the domain of supply chain management who would be interested in understanding how these technologies can enhance efficiency, transparency, and optimization in their supply chains.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You'll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data. You'll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you'll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mized syntax, allowing it to carry out various processes without superfluous lines of code. You'll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists who are new to using Wolfram and Mathematica as a programming language or tool. Programmers should have some prior programming experience, but can be new to the Wolfram language.

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

This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will Learn Understand what anomaly detection is, why it it is important, and how it is appliedGrasp the core concepts of machine learning.Master traditional machine learning approaches to anomaly detection using scikit-kearn.Understand deep learning in Python using Keras and PyTorchProcess data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recallApply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.

Gain a solid foundation for Natural Language Processing (NLP) and Large Language Models (LLMs), emphasizing their significance in today's computational world. This book is an introductory guide to NLP and LLMs with Python programming. The book starts with the basics of NLP and LLMs. It covers essential NLP concepts, such as text preprocessing, feature engineering, and sentiment analysis using Python. The book offers insights into Python programming, covering syntax, data types, conditionals, loops, functions, and object-oriented programming. Next, it delves deeper into LLMs, unraveling their complex components. You'll learn about LLM elements, including embedding layers, feedforward layers, recurrent layers, and attention mechanisms. You'll also explore important topics like tokens, token distributions, zero-shot learning, LLM hallucinations, and insights into popular LLM architectures such as GPT-4, BERT, T5, PALM, and others. Additionally, it covers Python libraries like Hugging Face, OpenAI API, and Cohere. The final chapter bridges theory with practical application, offering step-by-step examples of coded applications for tasks like text generation, summarization, language translation, question-answering systems, and chatbots. In the end, this book will equip you with the knowledge and tools to navigate the dynamic landscape of NLP and LLMs. What You'll Learn Understand the basics of Python and the features of Python 3.11 Explore the essentials of NLP and how do they lay the foundations for LLMs. Review LLM components. Develop basic apps using LLMs and Python. Who This Book Is For Data analysts, AI and Machine Learning Experts, Python developers, and Software Development Professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.

This book is your comprehensive guide to building robust Generative AI solutions using the Databricks Data Intelligence Platform. Databricks is the fastest-growing data platform offering unified analytics and AI capabilities within a single governance framework, enabling organizations to streamline their data processing workflows, from ingestion to visualization. Additionally, Databricks provides features to train a high-quality large language model (LLM), whether you are looking for Retrieval-Augmented Generation (RAG) or fine-tuning. Databricks offers a scalable and efficient solution for processing large volumes of both structured and unstructured data, facilitating advanced analytics, machine learning, and real-time processing. In today's GenAI world, Databricks plays a crucial role in empowering organizations to extract value from their data effectively, driving innovation and gaining a competitive edge in the digital age. This book will not only help you master the Data Intelligence Platform but also help power your enterprise to the next level with a bespoke LLM unique to your organization. Beginning with foundational principles, the book starts with a platform overview and explores features and best practices for ingestion, transformation, and storage with Delta Lake. Advanced topics include leveraging Databricks SQL for querying and visualizing large datasets, ensuring data governance and security with Unity Catalog, and deploying machine learning and LLMs using Databricks MLflow for GenAI. Through practical examples, insights, and best practices, this book equips solution architects and data engineers with the knowledge to design and implement scalable data solutions, making it an indispensable resource for modern enterprises. Whether you are new to Databricks and trying to learn a new platform, a seasoned practitioner building data pipelines, data science models, or GenAI applications, or even an executive who wants to communicate the value of Databricks to customers, this book is for you. With its extensive feature and best practice deep dives, it also serves as an excellent reference guide if you are preparing for Databricks certification exams. What You Will Learn Foundational principles of Lakehouse architecture Key features including Unity Catalog, Databricks SQL (DBSQL), and Delta Live Tables Databricks Intelligence Platform and key functionalities Building and deploying GenAI Applications from data ingestion to model serving Databricks pricing, platform security, DBRX, and many more topics Who This Book Is For Solution architects, data engineers, data scientists, Databricks practitioners, and anyone who wants to deploy their Gen AI solutions with the Data Intelligence Platform. This is also a handbook for senior execs who need to communicate the value of Databricks to customers. People who are new to the Databricks Platform and want comprehensive insights will find the book accessible.