Wydawca: Packt Publishing
Erik Benner, Hicham Assoudi, Tural Gulmammadov
In A Practical Guide to Oracle AI Engineering, you'll learn how to tackle the challenges of building scalable, high-performance AI workflows in modern enterprises. Many organizations struggle to turn raw data into actionable insights while maintaining security, compliance, and operational efficiency. This book provides practical, end-to-end guidance for data engineers and architects to design, secure, implement, and optimize ML and GenAI solutions across Oracle Cloud, Oracle Database, and MySQL HeatWave.Written by multiple Oracle experts with deep experience in Oracle technologies and enterprise data platforms, this book walks you through real-world examples and hands-on workflows, from data preparation and in-database ML to deploying GenAI-powered applications and intelligent agents. You’ll gain skills in building pipelines, managing models, leveraging vector search for advanced AI use cases, and integrating AI into business applications with APEX and Oracle Digital Assistant. Advanced topics include scalable model deployment, serverless inference, monitoring, and MLOps best practices. By the end, you’ll be equipped to solve complex data challenges, accelerate AI adoption, and deliver measurable business impact through intelligent, production-ready solutions.
A Practical Guide to Quantum Computing. Hands-on approach to quantum computing with Qiskit
Elías F. Combarro, Samuel González-Castillo, Ismael Faro...
This book is an introduction, from scratch, to quantum computing and the most important and foundational quantum algorithms—ranging from humble protocols such as Deutsch’s algorithm to ones with far-reaching potential, such as Shor’s factoring algorithm—offering clear explanations and a hands-on approach with runnable code on simulators and real hardware. The book is self-contained and does not assume any previous experience in quantum computing. Starting with a single qubit, it scales to algorithms using superposition and entanglement.At every step, examples of applications are provided, including how to create quantum money that is impossible to forge, quantum cryptography that cannot be broken, and algorithms for searching and factoring that are much faster than those that regular, non-quantum computers can use. Code for each of these algorithms is provided (and explained in detail) using Qiskit 2.1.After reading this book, you will understand how quantum algorithms work, how to write your own quantum programs, and how to run them on quantum simulators and actual quantum computers. You will also be prepared to take the jump into quantum algorithms for optimization and artificial intelligence, like those presented in our previous book, A Practical Guide to Quantum Machine Learning and Quantum Optimization.
Elías F. Combarro, Samuel González-Castillo
This book provides deep coverage of modern quantum algorithms that can be used to solve real-world problems. You’ll be introduced to quantum computing using a hands-on approach with minimal prerequisites.You’ll discover many algorithms, tools, and methods to model optimization problems with the QUBO and Ising formalisms, and you will find out how to solve optimization problems with quantum annealing, QAOA, Grover Adaptive Search (GAS), and VQE. This book also shows you how to train quantum machine learning models, such as quantum support vector machines, quantum neural networks, and quantum generative adversarial networks. The book takes a straightforward path to help you learn about quantum algorithms, illustrating them with code that’s ready to be run on quantum simulators and actual quantum computers. You’ll also learn how to utilize programming frameworks such as IBM’s Qiskit, Xanadu’s PennyLane, and D-Wave’s Leap.Through reading this book, you will not only build a solid foundation of the fundamentals of quantum computing, but you will also become familiar with a wide variety of modern quantum algorithms. Moreover, this book will give you the programming skills that will enable you to start applying quantum methods to solve practical problems right away.
Sandip Kulkarni
Reinforcement Learning from Human Feedback (RLHF) is a powerful approach to AI alignment and human-centered machine learning. By combining reinforcement learning algorithms with human feedback signals, RLHF has become a key method for improving the safety, reliability, and alignment of large language models (LLMs).This book begins with the foundations of reinforcement learning and policy optimization, including algorithms such as proximal policy optimization (PPO), and explains how reward models and human preference learning help fine-tune AI systems and generative AI models. You’ll gain practical insight into how RLHF pipelines optimize models to better match human preferences and real-world objectives.You’ll also explore strategies for collecting human feedback data, training reward models, and improving LLM fine-tuning and alignment workflows. Key challenges—including bias in human feedback, scalability of RLHF training, and reward design—are addressed with practical solutions.The final chapters examine advanced AI alignment methods, model evaluation, and AI safety considerations. By the end, you’ll have the skills to apply RLHF to large language models and generative AI systems, building AI applications aligned with human values.
Keith D. Sutherland, Lawrence J. "Butch" Sheets
Many organizations struggle to find practical guidance that can help them to not only understand but also apply service management best practices. Packed with expert guidance and comprehensive coverage of the essential frameworks, methods, and techniques, this book will enable you to elevate your organization’s service management capability.You’ll start by exploring the fundamentals of service management and the role of a service provider. As you progress, you’ll get to grips with the different service management frameworks used by IT and enterprises. You'll use system thinking and design thinking approaches to learn to design, implement, and optimize services catering to diverse customer needs.This book will familiarize you with the essential process capabilities required for an efficient service management practice, followed by the elements key to its practical implementation, customized to the organization’s business needs in a sustainable and repeatable manner. You’ll also discover the critical success factors that will enhance your organization’s ability to successfully implement and sustain a service management practice.By the end of this handy guide, you’ll have a solid grasp of service management concepts, making this a valuable resource for on-the-job reference.
Samer Najia
Until recently, Computer Numerical Control (CNC) machines belonged to the realm of heavy industry, but as technology becomes cheaper and smaller, these machines now can be used in home workshops. It’s not easy to get started, though, but thanks to this guide, you’ll be ready to take on a variety of projects in no time.A Tinkerer’s Guide to CNC Basics contains everything you need to get set up at home with computer-controlled machining and fabrication. Sparing you the theory, this project-laden guide helps you learn by doing.Once you’ve got to grips with the principles of CNC and installed the 3018 Pro CNC machine, you’ll gradually move from simple projects such as basic engraving to more complex milling and machining techniques. You’ll even learn how to upgrade your machine to accomplish more sophisticated designs. The plethora of projects in this book will keep you busy and give you the practice you need to get started with your computerized workshop.By the end of the book, your computerized home workshop will be one step closer to realization, and your machining skills will be taken to the next level.
Vadim Dabravolski
Over the past 10 years, deep learning has grown from being an academic research field to seeing wide-scale adoption across multiple industries. Deep learning models demonstrate excellent results on a wide range of practical tasks, underpinning emerging fields such as virtual assistants, autonomous driving, and robotics. In this book, you will learn about the practical aspects of designing, building, and optimizing deep learning workloads on Amazon SageMaker. The book also provides end-to-end implementation examples for popular deep-learning tasks, such as computer vision and natural language processing. You will begin by exploring key Amazon SageMaker capabilities in the context of deep learning. Then, you will explore in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMaker. You will learn how to train and serve deep learning models using popular open-source frameworks and understand the hardware and software options available for you on Amazon SageMaker. The book also covers various optimizations technique to improve the performance and cost characteristics of your deep learning workloads.By the end of this book, you will be fluent in the software and hardware aspects of running deep learning workloads using Amazon SageMaker.
Michael Kaufmann
This practical guide to DevOps uses GitHub as the DevOps platform and shows how you can leverage the power of GitHub for collaboration, lean management, and secure and fast software delivery.The chapters provide simple solutions to common problems, thereby helping teams that are already on their DevOps journey to further advance into DevOps and speed up their software delivery performance. From finding the right metrics to measure your success to learning from other teams’ success stories without merely copying what they’ve done, this book has it all in one place. As you advance, you’ll find out how you can leverage the power of GitHub to accelerate your value delivery – by making work visible with GitHub Projects, measuring the right metrics with GitHub Insights, using solid and proven engineering practices with GitHub Actions and Advanced Security, and moving to event-based and loosely coupled software architecture.By the end of this GitHub book, you'll have understood what factors influence software delivery performance and how you can measure your capabilities, thus realizing where you stand in your journey and how you can move forward.