Description
Artificial intelligence, edge computing, and data engineering are transforming how intelligent products are designed, deployed, and managed. As systems evolve from cloud-dependent to distributed intelligence, engineers must understand how to build solutions that combine data, models, and hardware into cohesive, scalable architectures. This book bridges the gap between conceptual AI knowledge and real-world system implementation, offering a hands-on guide to building next-generation intelligent products.

This book takes readers through the entire lifecycle of intelligent system development, from product design to model deployment and system integration. It begins with the foundational concepts of AI product architecture and progresses to practical design principles for building data pipelines, implementing MLOps, and developing edge-AI and IoT systems. Each chapter blends theory with implementation guidance and covers tools such as TensorFlow, PyTorch, Kafka, Spark, MLflow, and Kubernetes. Readers will also explore topics like cloud-edge orchestration, system monitoring, security and compliance, and platform-based product scaling through case studies.

By the end of this book, readers will be able to design and deploy AI, IoT, and edge systems with technical confidence. They will be equipped to make architectural trade-offs, optimize performance, and manage data-driven products across hybrid environments.

Description
As AI and ML rapidly power modern digital services from recommendation engines to generative models, moving these workloads into production exposes critical gaps in traditional operations. Site reliability engineering (SRE) applies software engineering principles to infrastructure, making SRE uniquely positioned to own the reliability, observability, and resilience of complex AI-driven environments.

This book bridges the gap between traditional SRE practices and the innovative strategies required to manage AI-driven infrastructures such as LLM models effectively, offering readers a focused guide for this transformative era. Each chapter walks the reader through core concepts such as versioning of models and data, pipeline monitoring, and security testing for AI APIs, while providing concrete SRE patterns for uptime, rollback, and incident management in AI-driven environments. You will learn how to design service-level objectives that reflect AI-specific quality metrics, implement robust monitoring and alerting for model drift and data quality, secure AI-backed APIs against prompt injection and other attacks, and coordinate releases across code, data, and models in a complex environment.

By the end of this book, as an SRE engineer, you will be equipped to design, operate, and scale AI systems with the same rigor that you applied to traditional infrastructure. You will gain skills in building observable AI pipelines, managing versioned data and models at scale, securing AI APIs, and applying SRE principles such as error budgets, incident response, and automation to complex ML and generative AI workloads.

À travers une perspective historique et analytique couvrant la période de 1945 à 2025, cet ouvrage explore la transformation des systèmes de santé dans les États membres de la CEMAC (Cameroun, Centrafrique, Congo, Gabon, Guinée équatoriale, Tchad).
À l’intersection entre orientations sanitaires mondiales et réalités socio-économiques locales, l’auteur analyse les processus d’appropriation, d’adaptation et de négociation des politiques publiques de santé. Face aux crises épidémiologiques, aux contraintes financières et aux défis structurels, ce livre offre un diagnostic sans complaisance de la gouvernance sanitaire régionale. Il met en évidence les réussites, identifie les vulnérabilités et propose des orientations stratégiques pour construire des systèmes de santé résilients, équitables et souverains au service des populations d’Afrique centrale. Un outil indispensable pour chercheurs, décideurs et acteurs du développement.

Les vignobles du futur Face au changement climatique, aux impasses du Bio et de la Biodynamie, et à la révolution technologique en marche, le vignoble du XXIe siècle a besoin d’un nouveau cadre de référence. Cet ouvrage propose l’Éco Méta-Éthique (EME) : une agroécologie rationnelle, universelle et éthique, synthèse de cinquante ans de recherches au sein du GiESCO. À travers l’évaluation comparative de 10 systèmes de conduite de la vigne, la définition d’une charte pratique et des exemples concrets en Italie et en France, les auteurs guident le vigneron vers les meilleurs choix possibles – agronomiques, climatiques, économiques et humains. Robots, humanoïdes, transfert technologique : les outils du futur sont déjà là. Il reste à les intégrer avec intelligence et éthique. Un guide inédit pour les vignerons, techniciens et professionnels de la filière vitivinicole. The vineyards of the future A third ethical path beyond Organics and Biodynamics 10 basic vineyard training systems evaluated Technology transfer, robots and humanoids Faced with climate change, the limitations of Organic and Biodynamic farming, and an accelerating technological revolution, the 21st-century vineyard needs a new framework. This book introduces Eco Meta-Ethics (EME): a rational, universal and ethical agroecology, the synthesis of fifty years of research within GiESCO. Through a comparative evaluation of 10 vine training systems, the definition of a practical charter, and concrete case studies in Italy and France, the authors guide the winegrower toward the best possible choices – agronomic, climatic, economic and human. Robots, humanoids, technology transfer: the tools of the future are already here. What remains is to integrate them with intelligence and ethics. An essential guide for winegrowers, technicians and professionals in the wine industr

Build confidence using ChatGPT for learning, writing, creativity, and everyday problem-solving. Write stronger prompts, evaluate AI responses, and use conversational AI more safely and effectively.

Key Features

• Plain-English guidance that makes ChatGPT approachable for readers with no technical background
• Real-life examples showing why thoughtful prompts produce clearer and more useful AI responses
• Balanced coverage of how to use AI creatively while recognizing privacy risks and limitations

Book Description

What you will learn

• Set up and use ChatGPT with confidence
• Write clearer prompts and effective follow-up questions
• Use ChatGPT as a tutor for self-paced learning
• Brainstorm, write, and solve everyday problems with AI
• Create images, stories, songs, and games with ChatGPT
• Recognize hallucinations, privacy risks, and AI limitations

Who this book is for

Complete beginners, non-technical readers, lifelong learners, writers, educators, professionals, and curious everyday users who want practical help with ChatGPT. No programming knowledge or prior ChatGPT experience is required; the material suits anyone seeking clearer prompts, useful AI-assisted learning, creative support, and safer everyday use.

Build and validate a governed AI-native developer platform with Claude, Kubernetes, GitOps, policy controls, observability, and reproducible workflows.

Key Features

• Control Claude with specifications, permissions, tests, and auditable workflows
• Build governed agent and model infrastructure on Kubernetes with GitOps
• Create a Backstage self-service path from developer request to traced agent
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

What you will learn

• Control Claude with specifications, permissions, and test gates
• Design an AI-native IDP with clear ownership and trust boundaries
• Build a reproducible Kubernetes foundation with Argo CD
• Trace platform, agent, and model activity with OpenTelemetry
• Govern LLM, MCP, and agent traffic through shared gateways
• Run Kubernetes-native agents with guardrails and policy controls
• Serve OpenAI-compatible models using KServe and vLLM
• Build a governed Backstage self-service path for agent services

Who this book is for

Platform engineers, DevOps engineers, SREs, cloud engineers, and platform architects who want to use Claude to build and operate governed AI capabilities on Kubernetes. Engineering and technical leads can also use the architecture and production guidance to assess scope and risk. Experience with Kubernetes, Helm, and GitOps is required; prior Backstage experience is not.

Description
Agentic AI marks the most significant shift in enterprise technology since cloud computing. Autonomous systems that can reason, coordinate across tools, and act with bounded independence are rewriting how organizations operate, make decisions, and deliver value at scale.

This book takes you from foundational concepts to production-ready execution. Starting with the evolution from macros and automation scripts to goal-driven agents, you will learn how agentic AI replaces brittle automation with adaptive orchestration. Real finance workflows, frameworks for identifying high-value use cases, agent-driven operation design, cross-functional collaboration protocols, responsible AI practices, infrastructure readiness, a 90-day execution playbook, and organizational transformation are all covered. Industry deep dives into healthcare and financial services show agents handling regulated, high-stakes work. Final chapters address scaling from pilot to platform and thriving in an agent-first future.

By the end of this book, you will be equipped to lead agentic AI initiatives with clarity on architecture, governance, talent strategy, and measurable business impact across any enterprise domain.

Rédigé par plusieurs spécialistes français du sujet et richement illustré en couleurs, cet ouvrage dresse un panorama complet des connaissances actuelles sur l’histoire de l’humanité et fait le point sur les questions encore en suspens. Il détaille la démarche scientifique de la recherche à partir de la découverte de fossiles sur le terrain et donne des points de repère historiques permettant de recontextualiser les découvertes dans l’histoire des idées et de la recherche.
L’histoire de l’humanité y est exposée chronologiquement, illustrée de planches morphologiques comparatives entre les espèces, et complétée par une approche transversale fonctionnelle (locomotion, manipulation, alimentation, cérébralisation...) qui présente leur diversité et leurs changements au cours du temps et permet de comprendre leurs liens éventuels, de reconstituer leur mode de vie et de comprendre leurs stratégies d’adaptation ou de dispersion.
Des fiches récapitulatives par espèce et un glossaire complètent l’ouvrage pour réviser efficacement.

Go from Linux fundamentals to administering Ubuntu Server 26.04 across on-premises and AWS environments, covering networking, security, virtualization, Docker, MicroK8s, Ansible, Terraform, local AI with Lemonade, and Landscape

Key Features

• Get up to speed with the latest features and capabilities of Ubuntu Server 26.04
• Master installing, managing, securing, and troubleshooting Ubuntu Server
• Automate with Ansible and Terraform, deploy MicroK8s, and run local AI with Lemonade

Book Description

What you will learn

• Install and configure Ubuntu Server 26.04 on multiple platforms
• Manage users, permissions, packages, processes, and storage
• Configure Netplan, SSH, DNS, DHCP, gateways, Samba, and NFS
• Deploy databases, web servers, virtual machines, and containers
• Automate server and AWS deployments with Ansible and Terraform
• Run local LLMs with Lemonade and isolate AI agents with AI Lab
• Deploy Kubernetes workloads with MicroK8s and Ubuntu on AWS
• Secure, troubleshoot, back up, and recover Ubuntu servers

Who this book is for

This book is for Linux system administrators, DevOps and cloud engineers, site reliability engineers, infrastructure professionals, homelab users, and IT enthusiasts who want to build practical Ubuntu Server skills. It is also suitable for readers pursuing Canonical Academy's SysAdmin qualification or moving into Linux administration. No previous Ubuntu Server experience is required, although familiarity with basic computing concepts is helpful. Prior Linux, IT administration, or shell experience will help readers progress through the advanced topics more quickly.

Les organismes qui survivent sont ceux qui s’entraident le plus. Cette coopération s’organise par le transfert de structures. Nous portons les gènes de nos ancêtres, éventuellement modifiés, partagés par les individus d’une même espèce mais aussi entre les organismes par transferts. Nous sommes le résultat d’un métissage cellulaire et portons l’entière culture du monde vivant, de celui qui a précédé comme de celui côtoyé. Les mitochondries sont impliquées dans les informations cellulaires et les biosynthèses ; elles sont partie prenante de ces transferts et ont une importance primordiale dans le fonctionnement, l’adaptation et la santé des organismes. Elles participent aussi à différentes pathologies.  Issues d’une symbiose de cellules, elles sont un lieu de formation de nouveaux gènes. L’obligation de subir suffit à légitimer le droit, mais aussi le devoir, de savoir, sans oublier que la science est une arme de réflexion massive, et que beaucoup trop la craignent. En ces temps où les échanges migratoires sont dénoncés, il est nécessaire de se souvenir que ce dynamisme et ces coopérations sont intrinsèques au développement de la vie, du moléculaire jusqu’à la société.

Apply AI to Domain-Driven Design with LLMs, prompt engineering, and multi-agent systems. Build repeatable domain modeling workflows that strengthen software architecture decisions, domain clarity, and consistency.

Key Features

• Apply LLMs as structured reasoning partners for Domain-Driven Design
• Advance domain modeling with structured prompts and EventStorming
• Design AI agents and multi-agent systems for DDD workflows
• Strengthen software architecture decisions with AI-assisted reasoning
• Refine domain events, aggregates, and bounded contexts with AI

Book Description

What you will learn

• Understand how LLMs reason, where they help, and where they fail
• Apply prompt engineering to Domain-Driven Design workflows
• Use AI-assisted reasoning to refine domain events and bounded contexts
• Build rulebooks that guide and constrain LLM reasoning
• Apply AI to EventStorming and domain modeling activities
• Design AI agents for domain discovery and DDD workflows
• Orchestrate multi-agent systems for domain modeling
• Strengthen software architecture decisions while preserving domain clarity

Who this book is for

This book is for software developers, software architects, and technical leaders working on complex business systems who want to advance their Domain-Driven Design practice with AI. You should be comfortable with software architecture concepts and familiar with DDD fundamentals such as bounded contexts, aggregates, domain events, and domain modeling. No prior AI expertise is required, but an interest in LLMs, prompt engineering, and AI-assisted reasoning will help you get the most from the practical workflows.

Description
This book is a project-based guide designed to bridge the gap between basic electronics and modern intelligent systems. The Raspberry Pi has transformed physical computing, the Internet of Things (IoT), and edge artificial intelligence by making powerful computing accessible to everyone.

Along with the origins, setup, and troubleshooting of the Raspberry Pi, this book guides you through Linux terminal navigation, file management, and beginner-friendly Python programming. Readers learn to configure Raspberry Pi OS and develop essential Linux command-line skills for working with Raspberry Pi-based systems. You will also learn Python programming for hardware automation and device control. With these fundamentals in place, the focus shifts to hardware interfacing. You will learn to interface with real-world sensors such as the BME280 for environmental monitoring, ultrasonic sensors for distance measurement, and LDRs for light detection.

By the end of this book, you will have the practical skills to build real-world electronics and IoT projects, develop computer vision applications, and deploy AI solutions on the Raspberry Pi.

Turn AI into a real source of income with five proven side hustles, realistic earning expectations, and a 90-day plan to build recurring revenue without coding

Key Features

• Explore five AI side hustles with realistic income ranges and startup costs
• Start earning with AI through opportunities that require little or no coding
• Follow a 90-day launch plan to move from your first client to recurring revenue

Book Description

What you will learn

• Choose an AI side hustle that matches your existing strengths
• Start AI freelancing and find your first paying clients
• Sell AI automation services to small businesses
• Build and monetize SaaS products using vibe coding
• Create scalable AI-assisted content businesses
• Package prompts and custom AI solutions as paid services
• Market your services through outreach, referrals, and case studies
• Follow a 90-day plan toward repeatable, recurring revenue

Who this book is for

This book is for professionals, freelancers, creators, entrepreneurs, and aspiring side-hustlers who want to use AI to build an additional source of income without falling for get-rich-quick promises. It's particularly suited to beginners who want realistic guidance on where to start, what they can earn, and how much time and money each path requires. Most of the opportunities require no coding experience.

Description
Machine learning has become a cornerstone of modern technology, driving innovations across industries such as healthcare, finance, energy, and automation. As organizations increasingly rely on Data-driven decision-making, a strong understanding of machine learning fundamentals is essential for both students and professionals to remain relevant and effective in their roles.

This book provides a structured and intuitive learning experience through a Q&A format. It begins with foundational machine learning concepts, followed by detailed coverage of supervised and unsupervised learning techniques. The book explores key algorithms such as logistic regression, support vector machines (SVM), decision trees, and ensemble methods. It then progresses to advanced topics, including neural networks, dimensionality reduction, and modern trends like large language models (LLMs). Dedicated chapters on practical implementation using Python libraries, real-world applications, and interview-focused questions ensure a well-rounded understanding of both theory and practice.

By the end of this book, readers will have developed strong conceptual clarity and practical insight into machine learning techniques. They will be equipped to apply these concepts in real-world scenarios, approach problems with confidence, and perform effectively in academic, research, or industry roles.

Description
Data science is transforming modern tech, and mastering it is essential for anyone aiming to analyze data, build predictive models, and deploy cutting-edge AI systems. This Data Science Cookbook is your comprehensive roadmap to understanding core analytics, classical machine learning, and advanced generative AI using a hands-on, practical approach.

This book systematically guides you through the complete data science lifecycle, starting with environment setup, foundational mathematics, exploratory data analysis, and visualization. You will master classical supervised and unsupervised machine learning algorithms, ensemble methods, and neural network architectures. The guide further explores computer vision with OpenCV, natural language processing, transformer mechanics, generative AI, GANs, and parameter-efficient LLM fine-tuning using LoRA and prompt engineering. Backed by 127 recipes, it covers MLOps production deployment with Flask APIs, Azure ML, and model drift monitoring, concluding with capstone projects featuring real-time face detection, GPT-3 chatbots, synthetic image generation, and vector database image comparison.

By the end of this book, you will possess a job-ready understanding of data science, machine learning, and artificial intelligence, equipped with the practical skills needed to build, deploy, and scale real-world applications confidently.

Face aux enjeux actuels de l’agriculture – hausse du coût des intrants, dégradation des sols, défis environnementaux – les engrais verts (ou couverts végétaux) apparaissent comme une solution à la fois ancienne et résolument moderne. Dans cet ouvrage de référence, Joseph Pousset explore en profondeur le rôle des couverts végétaux dans le maintien et l’amélioration de la fertilité des sols. S’appuyant sur des observations de terrain, des recherches scientifiques et une longue expérience agronomique et de pratique agricole, il met en lumière les mécanismes naturels souvent méconnus qui régissent la vie du sol. De la formation des terres fertiles aux interactions complexes entre plantes, micro-organismes et climat, ce livre propose une approche globale et accessible. Il offre également des conseils pratiques pour choisir, implanter et valoriser les engrais verts dans différents systèmes agricoles. À la croisée de la tradition et de l’innovation, cet ouvrage invite agriculteurs, étudiants et passionnés à repenser leur manière de cultiver, en s’inspirant des équilibres naturels

Build evaluation systems that reveal whether your LLM applications are improving or regressing, using calibrated judges, RAG and agent metrics, CI regression tests, online evals, and cost-aware pipelines

Key Features

• Build a complete Python eval harness from datasets and scorers to CI and dashboards
• Evaluate RAG, agents, and prompts with calibrated metrics and human feedback
• Move from offline testing to production evals, guardrails, and reliability workflows

Book Description

What you will learn

• Design eval datasets, scorers, runners, and golden test sets
• Build deterministic metrics for repeatable quality checks
• Calibrate LLM judges and reduce common evaluation bias
• Add prompt regression tests to continuous integration
• Measure retrieval and generation quality in RAG systems
• Evaluate agent trajectories, tool calls, and multi-turn behavior
• Run human annotation workflows and production online evals
• Control evaluation cost and latency without losing signal

Who this book is for

This book is for AI engineers, LLM application developers, machine learning engineers, platform engineers, and technical leads who build or operate production systems using large language models. It is especially useful for teams working with prompts, RAG pipelines, agents, or AI features that need measurable quality and regression protection. Readers should be comfortable with Python and familiar with building or integrating LLM applications.

Gain practical skills to optimize C++ programs using modern CPU architectures, memory models, and concurrency tools

Key Features

• Apply advanced CPU, memory, and concurrency techniques to real-world code
• Design efficient thread-safe data structures using lock-free and atomic strategies
• Leverage compiler behavior and modern C++ standards for performance gains

Book Description

What you will learn

• Analyze and optimize CPU resource usage
• Improve memory access patterns across architectures
• Design performant multithreaded and lock-free systems
• Use atomic operations and smart pointers effectively
• Apply memory order guarantees for correctness and speed
• Evaluate compiler decisions and leverage vectorization
• Implement coroutine-based execution models
• Build APIs that scale in concurrent environments

Who this book is for

Experienced C++ developers, performance-focused programmers, and engineers working in high-speed systems like trading platforms, games, or scientific computing. Prior knowledge of multithreading and memory management is required to make the most of this book.

Description
Python is increasingly used for scientific computing, data handling, automation, and reproducible analysis. In pharmaceutical sciences, it can support structured work with laboratory observations, formulation data, quality-control records, pharmacokinetic datasets, ADR reports, inventory information, and research data.

Python for Pharmaceutical Sciences introduces programming from the beginning for B.Pharm learners and is aligned with the core coverage of BP101T. It covers Jupyter Notebook, variables, data types, input/output, operators, strings, conditions, loops, functions, data structures, NumPy, file handling, Pandas, data cleaning, and Matplotlib. Each concept is supported with pharmacy-focused examples, algorithms, executable code, outputs, interpretations, exercises, and integrated projects.

By the end of this book, readers will be able to write basic Python programs, organize and analyze pharmaceutical datasets, automate routine calculations, and communicate findings through tables and visualizations. No prior programming experience is required; examples and datasets are intended for educational use.

Alors que la transition énergétique mondiale s’accélère, les pays producteurs de pétrole, de gaz et de charbon doivent repenser leurs trajectoires de développement historiquement fondées sur la rente fossile. Leur vulnérabilité aux fluctuations des marchés internationaux, conjuguée à l’éventualité d’un déclin structurel de la demande, met à l’épreuve la soutenabilité de leurs modèles économiques. Leur avenir dépendra largement de leur capacité à identifier de nouveaux leviers de croissance et à engager des stratégies de diversification, qu’elles soient énergétiques, industrielles ou financières.

Cet ouvrage collectif, rédigé par la promotion 2023 du Mastère spécialisé Optimisation des systèmes énergétiques (OSE) de Mines Paris – PSL, propose une analyse structurée et pluridisciplinaire des perspectives d’adaptation possibles pour ces pays. Il s’ouvre sur une typologie des pays producteurs puis examine les incertitudes affectant l’évolution de la demande mondiale en combustibles fossiles. L’analyse se poursuit par une évaluation comparative des potentiels de diversification énergétique, puis s’élargit aux stratégies de diversification extra-énergétique et aux reconfigurations géopolitiques, économiques et sociales susceptibles d’accompagner une sortie progressive du régime fossile.

L’ensemble éclaire les dynamiques profondes de transformation à l’oeuvre dans les économies exportatrices d’hydrocarbures à l’heure des transitions globales.

Description
Modern organizations face thousands of vulnerabilities across applications, cloud environments, containers, APIs, and software supply chains. The challenge today is not just to find vulnerabilities, but to understand which ones matter most and fix them before attackers can exploit them. As cloud adoption, DevOps, and AI-driven development continue to grow, vulnerability management has become one of the most important and complex areas of cybersecurity.

Practical Vulnerability Management takes readers through the full lifecycle of building and running an effective vulnerability management program. The book covers core foundations, asset discovery, risk-based prioritization, threat intelligence, cloud-native security challenges, remediation workflows, fix validation, governance, compliance, audit readiness, and program effectiveness measurement. It also explores lessons from major real-world incidents and explains how AI can improve vulnerability discovery, triage, remediation, and operational decision-making.

By the end of this book, readers will be able to design stronger vulnerability management workflows, improve prioritization based on real business risk, reduce exposure faster, and build scalable programs that align security with engineering and business goals. This book equips security leaders, engineers, and practitioners with the practical skills needed to move beyond scanning and build stronger, more resilient security operations.

Viruses never cease to fascinate and frigthen us because of their pathogenic nature for plants and animals. In seas and oceans, they are major players in the functioning of ecosystems. Viruses are the smallest, most abundant, and most diverse biological entities known to date in marine environments, where they perform multiple functions. In addition to their role in cellular mortality and the redistribution of matter, viruses are also powerful drivers of the evolution of their hosts and represent a major source of genetic diversity, offering rich opportunities for discoveries in both fundamental and applied research. The aim of this book is to highlight an invisible biological community that is essential for understanding and explaining how the oceans function. The authors, specialists in the ecology and evolution of aquatic viruses, show how viruses regulate microbial ecosystems—the foundation of oceanic food webs—and how their activity could, under certain circumstances, influence the climate. 

This book is intended for students in life and earth sciences, teachers, researchers, and academics, as well as for the general public interested in the subject of viruses and the oceans. 

Description
AI is becoming the backbone of industrial solutions; applications that help in the prediction of the life of a machine, face detection, text generation, and image generation are the results of great algorithms that are at work on huge datasets. However, these algorithms work on a simple concept, such as garbage in, garbage out; that is, if the data is not right, the algorithm will fail to give the expected results.

This crash course book outlines the practical issues that are faced by the industry while collecting data and how these problems can be solved using advanced tools that we have in the current market. This book shows the step-by-step implementation of how the filtering of the data can be done, what logic can be applied to get the best output of the target algorithm with the current dataset, and the book will also cover how the data is fed into the pipeline where it can be used for further processing and predicting the outcomes.

By the end of the book, readers will be prepared to align their thought process, starting from taking the basic steps to select the correct data, putting it into the right structure, optimizing processing and engineering on the same so that the right algorithm can be trained on this data to give the best results that will be in line with the expectation of the end customer.

Architect, orchestrate, and deploy scalable AI agents with real-world patterns, multi-agent systems, and evaluation frameworks.

Key Features

• Filled with real-world agentic web application examples using LLMs, RAG, and tool calling
• Design and implement AI assistants with reasoning patterns like ReAct
• Architect scalable agent systems with planning, orchestration, and autonomy

Book Description

What you will learn

• Integrate LLMs into web apps and extend their capabilities with tool calling and external systems
• Build RAG pipelines that ground AI applications in relevant business and application data
• Use MCP to connect AI applications with tools, resources, and capabilities
• Build intelligent AI agents using ReAct, advanced agent architectures, and multi-agent patterns
• Test and evaluate LLMs and agents to improve reliability and accuracy
• Build an agent harness with the context, tools, guardrails, and infrastructure needed to take AI applications into production

Who this book is for

This book is for full-stack developers, software architects, and AI practitioners looking to build intelligent, agent-driven web applications. It’s also valuable for tech leads and product managers working on AI-powered products. If you have a basic understanding of web development and want to move beyond simple AI integrations into autonomous, production-ready agent systems, this book will give you the frameworks, tools, and hands-on experience to get there.

Begin your professional options trading journey by learning the principles, creating methods, and using Python to thrive in the volatile trading market

Key Features

• Master option pricing with Python using Black-Scholes, binomial and trinomial trees, and Monte Carlo simulation
• Decode implied volatility and option Greeks to analyze sensitivities, valuation, and risk
• Apply option pricing across asset classes through real-world case studies, machine learning applications, portfolio optimization, and risk management
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

What you will learn

• Master Black-Scholes option pricing with Python
• Build binomial, trinomial, and Monte Carlo models
• Apply option pricing across FX, equity, rates, commodities, and other asset classes
• Decode implied volatility and volatility models
• Understand option Greeks and risk sensitivities
• Practice trading strategies, hedging, and portfolio optimization
• Price exotic options using numerical methods
• Apply machine learning techniques to options pricing and risk analysis

Who this book is for

This book is for capital markets professionals, quantitative and algorithmic traders, researchers, developers, and finance students who want to master option pricing with Python. Readers will learn to build and test pricing models, analyze implied volatility and Greeks, and apply Python to quantitative finance, trading, portfolio optimization, and risk management.

Learn the fundamentals, complete professional assignments with Cowork, create reliable automated workflows, and develop your skills in your current role, learn new skills before career switching, and design and sell services and products

Key Features

• Learn through concise lessons, readable screenshots, guided activities, and practical examples.
• Complete realistic projects that reflect how managers, analysts, and consultants work.
• Develop responsible AI practices with verification, privacy controls, and human approval.

Book Description

What you will learn

• Set up and secure your Claude account
• Write well-structured prompts and improve weak responses
• Search the web, assess sources, and analyze uploaded files
• Produce professional documents, spreadsheets, and presentations
• Plan and supervise multi-step assignments with Cowork
• Create reliable automated workflows with Skills and plugins
• Develop your skills in your current role and learn new skills
• Design and sell services and products

Who this book is for

This book is for managers, administrators, consultants, analysts, researchers, writers, marketers, sales professionals, project managers, educators, trainers, recruiters, freelancers, entrepreneurs, and career changers. It is designed for readers who are comfortable using web browsers, desktop applications, and common office files but require no previous experience with Claude, prompting, automation, agents, MCP, or running an AI-assisted business.

Description
Advanced Excel is a powerful tool that goes beyond basic spreadsheet functions, allowing users to perform complex calculations, manipulate large datasets, create interactive visualizations, automate tasks, and conduct in-depth data analysis. If you want to enhance your productivity, accuracy, and decision-making skills in Excel, look no further.

This book is a comprehensive guide that explores the advanced features of Microsoft Excel. From creating macros with VBA to automating tasks, working with large datasets, creating visualizations, and conducting data analysis, this book covers it all. Additionally, it also introduces ChatGPT, an AI chatbot that enhances Excel automation. With practical examples and clear instructions, this guide empowers users to maximize their productivity, efficiency, and decision-making skills in Excel. In this edition, six new chapters take your Excel skills further with ChatGPT, covering practical ways to create formulas, analyze data, automate tasks, generate VBA code, troubleshoot Excel problems, and improve everyday productivity with AI. The book combines advanced Excel techniques with practical AI applications to help professionals work smarter and more efficiently.

By the end of this book, you will have developed the expertise and confidence to tackle complex tasks in Excel with ease.

What you will learn
? Develop proficiency in working with large datasets.
? Conduct data analysis using powerful tools like VLOOKUP and advanced PivotTables.
? Create impactful charts and graphs that effectively communicate your findings and insights.
? In this new edition, explore six dedicated chapters on ChatGPT and Excel.
? Use ChatGPT to create, explain, and improve Excel formulas and solve complex Excel calculations.
? Apply ChatGPT to advanced formulas and complex Excel calculations.

Who this book is for
For students and individuals seeking to enhance their Excel skills for advanced tasks, this extensive book serves as a valuable asset. It also caters to the needs of accountants, financial analysts, business analysts, and data scientists who aspire to boost their efficiency and productivity in Excel.

Table of Contents
1. Excel 365
2. Cell References and Range
3. Working with Formulas and Functions
4. Data Validation
5. Protection
6. Sorting a Database
7. Filtering a Database
8. Subtotals and Data Consolidation
9. PivotTables
10. Conditional Formatting
11. What-if Analysis
12. Working with Multiple Worksheets, Workbooks and Applications
13. Working with Charts
14. Creating and Recording Macros in VBA
15. Assigning Buttons to Macros
16. Variables and Data Types in VBA
17. Functions and Subroutines in VBA
18. Conditional Statements in VBA
19. Looping Structures in VBA
20. Arrays and Collections in VBA
21. Debugging and Error Handling in VBA
22. User Forms and User Input in VBA
23. Advanced VBA Techniques and Best Practices
24. Building Custom Add-ins with VBA
25. ChatGPT with Excel
26. Using ChatGPT for Excel Formulas
27. Advanced Formulas with ChatGPT
28. Data Cleaning and Transformation with ChatGPT
29. Data Analysis with ChatGPT
30. Writing and Understanding Macros with ChatGPT
31. Refining and Debugging VBA with ChatGPT

Lead the AI revolution with confidence. Learn how to craft strategy, build AI teams, and drive innovation responsibly with this practical leadership playbook for the Generative AI era. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

• Build AI fluency and lead digital transformation with confidence
• Design enterprise-wide AI strategy, teams, and governance models
• Unlock growth with responsible and scalable Generative AI adoption

Book Description

What you will learn

• Understand generative and agentic AI capabilities and business value
• Build organizational AI literacy and cultural readiness
• Craft an enterprise AI strategy aligned with business goals
• Design and scale high-performing AI teams and structures
• Evaluate AI platforms and vendors for transparency and reliability
• Embed responsible AI governance and compliance frameworks

Who this book is for

This book is for AI professionals, AI business leaders, founders, and senior managers who need to navigate the strategic, operational, and ethical challenges of AI adoption. It’s also invaluable for consultants, policy leaders, and transformation specialists advising enterprises on AI readiness. No coding knowledge is required—only a vision to lead AI-powered change across industries.

Build real-world AI projects with Python, pandas, NumPy, Matplotlib, Seaborn, scikit-learn, NLP, neural networks, sentiment analysis, and web deployment as you move from first code to portfolio-ready AI apps and skills.

Key Features

• Build portfolio-ready AI projects with Python, data analysis, ML, NLP, and deployment
• Use pandas, NumPy, Matplotlib, Seaborn, scikit-learn, and neural networks on real datasets
• Move from zero programming to practical AI workflows through guided, hands-on projects

Book Description

What you will learn

• Set up Python for hands-on AI and machine learning projects
• Use core syntax, data types, control flow, functions, and libraries for AI work
• Clean, transform, analyze, and visualize data with NumPy, pandas, Matplotlib, and Seaborn
• Apply EDA to find patterns, missing values, relationships, and features
• Build prediction and classification models using scikit-learn workflows
• Understand neural network basics and practical deep learning concepts
• Create an NLP sentiment analysis model from text data
• Deploy a trained AI model as a web service

Who this book is for

This book is for absolute beginners, students, career changers, junior developers, analysts, data enthusiasts, and hobbyists who want a practical entry point into AI development. It is useful for readers building their first AI portfolio, exploring machine learning or data science roles, or learning how trained models become simple applications. No prior programming or AI experience is required.

Description
Every device in a network, from routers to applications and IoT devices, produces large volumes of logs, metrics, and traces. Commercial observability systems can process this, but they often mean long contracts, higher costs, and sensitive data leaving the company. The Elastic Stack is the leading open-source solution for log management, system security, and enterprise observability. Practical Elastic Stack is your hands-on guide to mastering this powerful technology, designed to help you transform raw log data and security events into actionable operational intelligence.

Beginning with foundational concepts and hands-on installation, this practical guide walks through core architectural concepts, installation methods, and cluster setup across Linux, Windows, and Docker. You will master data transformation pipelines using Logstash, visualize live data, and build interactive dashboards in Kibana. It also covers agent management with Elastic Fleet, application monitoring with APM, and security monitoring with SIEM, with event correlation and detection rules in Kibana. You will learn to leverage the stack for security threat monitoring and set up automated alerting.

By the end of this book, readers will know what distinguishes monitoring from observability and how to build a scalable platform on their network. Upon completing this practical guide, readers possess the real-world skills to design, deploy, and optimize production-ready Elastic Stack environments to solve complex monitoring and security challenges.

Use ChatGPT, Claude, Aider, and AI-powered command-line tools to automate Linux administration, generate and debug Bash scripts, analyze logs, troubleshoot incidents, strengthen security, and build safe, repeatable operational workflows

Key Features

• Apply ChatGPT, Claude, Aider, and AI CLI tools to real Linux administration workflows
• Automate Bash scripting, troubleshooting, monitoring, log analysis, and routine maintenance
• Use practical safeguards to validate AI output and protect credentials, data, and production systems

Book Description

What you will learn

• Understand how generative AI supports modern Linux system administration
• Set up a safe Linux lab and configure AI-powered command-line tools
• Use ChatGPT and Claude to generate commands, scripts, and operational guidance
• Create, debug, and improve Bash scripts with AI assistance
• Analyze logs and troubleshoot services, performance, storage, and networking issues
• Automate recurring tasks, cron jobs, backups, updates, and user administration
• Apply AI to Linux hardening, configuration review, documentation, and runbooks

Who this book is for

This book is designed for Linux system administrators, DevOps engineers, cloud and platform engineers, SREs, IT support professionals, and learners who want to use generative AI in day-to-day Linux operations. It will help readers automate repetitive administration, generate and improve Bash scripts, troubleshoot services, analyze logs, document procedures, and strengthen system security using ChatGPT, Claude, Aider, and AI-powered CLI tools. Basic familiarity with Linux commands is helpful, but no prior AI or machine learning experience is required.

Reduce risk from unreliable AI outputs by learning how to detect hallucinations, verify claims, ground responses with RAG, design prompt guardrails, and monitor LLM workflows before flawed answers reach users or decisions.

Key Features

• Detect fabricated facts, weak citations, outdated claims, and unsafe AI outputs
• Use prompt guardrails, RAG, NotebookLM-style grounding, and model checks to improve LLM reliability
• Apply fact-checking, escalation, logging, and monitoring for safer AI adoption
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

What you will learn

• Explain why LLMs hallucinate and what makes outputs unreliable
• Detect fabricated facts and false references before they spread
• Reduce hallucinations with prompts, constraints, and guardrails
• Ground responses in trusted sources using RAG and vector databases
• Verify claims with source checks and model comparison for accuracy
• Build reliable AI systems with guardrails, logging, and fallbacks
• Apply compliance checks and bias tests for responsible deployment
• Monitor LLM apps in production with OpenTelemetry and Grafana

Who this book is for

This book is for Data scientists, AI engineers, developers, technical leads, product managers, compliance professionals, and business teams adopting LLMs in code, content, research, analytics, or decision-support workflows. This book is useful for those who need practical methods to reduce AI hallucinations, validate outputs, and communicate AI limitations clearly. No advanced programming experience is required, but familiarity with LLM tools, APIs, JSON, or command-line workflows will help.

Les nanosciences contribuent fortement aux technologies d’aujourd’hui. Les nanoparticules sont de nouveaux objets dont l’importance s’explique à partir de leurs propriétés de base décrites en détail dans un premier tome intitulé : « Les agrégats. Introduction au nanomonde ».
Dans ce deuxième tome, les auteurs s’intéressent aux excitations des agrégats, qu’elles soient thermiques, optiques ou autres.
Tout d’abord, ils décrivent leurs propriétés thermodynamiques. En raison du petit nombre d’atomes, les fluctuations sont très importantes et en particulier influencent de façon notable la température de fusion, ce qui est mesurable. De plus, il est possible d’observer la fragmentation des agrégats par collision avec des électrons ou des photons et de revisiter à l’échelle nanométrique le critère de Rayleigh pour l’explosion coulombienne. Leur ionisation en fonction de leur taille est également étudiée. Enfin les propriétés optiques des petits agrégats sont décrites, avec l’émergence de la résonance géante dite « résonance de plasmon de surface » qui n’existe ni dans l’atome, ni en matière condensée. Les conditions pour que ce phénomène apparaisse, sont discutées, ainsi que ses principales caractéristiques. Cette résonance géante confère aux agrégats des propriétés optiques particulièrement intéressantes du point de vue fondamental et pour de nombreuses applications.

Description
Agentic AI has moved from research demos to the core of how modern software is built. Powered by language models, autonomous agents now reason, remember, use tools, and collaborate to solve problems no single model could—reshaping how applications are designed across every industry.

The Agentic Paradigm is a practitioner's guide to building multi-agent systems end to end. It begins with the anatomy of an agent—perception, memory, reasoning, and action, and shows how to build one using LangChain and LangGraph. It then covers multi-agent communication, coordination patterns, and emerging interoperability protocols, before applying these ideas to real use cases across finance, manufacturing, and healthcare. The closing chapters address what separates a demo from production: testing, observability, security, and building responsible systems aligned with ethical, social, and legal standards.

By the end, you will be able to design, architect, optimize, secure, and observe multi-agent systems and judge confidently when an agentic design is the right choice and when a classical one will serve better.

Le pilotage d’une entreprise agricole ne peut plus se limiter à la seule maîtrise technique. Dans un contexte de forte volatilité, d’exigences économiques accrues et de transformations profondes du monde agricole, la gestion devient un levier décisif de pérennité, de performance et d’autonomie. Dans cette 3e édition, Raymond Levallois propose une approche claire, structurée et concrète de la gestion de l’entreprise agricole, articulée autour de ses dimensions économiques, financières et humaines. L’ouvrage aborde les grands fondements de la décision, de l’analyse du bilan et des résultats, du financement des investissements, de la gestion du risque, du diagnostic global et de l’accompagnement en conseil. Il s’appuie également sur une étude de cas complète, «la Ferme normande», pour passer de la théorie à la pratique. Destiné aux étudiants, aux enseignants, aux conseillers, ainsi qu’aux agriculteurs et agricultrices, ce guide privilégie une démarche opérationnelle, accessible et rigoureuse. Il rappelle une conviction forte : derrière chaque entreprise agricole, il y a d’abord des femmes et des hommes, leurs objectifs, leurs arbitrages et leur vision de l’avenir. Un ouvrage de référence pour comprendre, décider et gérer avec cohérence dans l’agriculture d’aujourd’hui

Bring FinOps discipline to production LLM systems by measuring AI spend, optimizing token usage, and building cost controls that scale with your applications

Key Features

• Build tokenwatch to measure, attribute, and optimize production LLM costs
• Reduce AI spend using caching, batching, model routing, and RAG optimization
• Establish budgets, quotas, forecasting, and accountability for sustainable AI

Book Description

What you will learn

• Measure token usage and attribute LLM costs accurately
• Calculate AI costs per request, user, and product feature
• Optimize prompts to reduce unnecessary token consumption
• Use prompt caching and calculate its break-even point
• Cut workload costs with batching and model routing
• Optimize RAG pipelines and control agent-related costs
• Evaluate the economics of APIs versus self-hosted models
• Build budgets, quotas, forecasts, and AI cost governance

Who this book is for

This book is for AI engineers, ML engineers, software engineers, platform engineers, technical leads, engineering managers, architects, and FinOps professionals responsible for building or operating LLM applications. It will also benefit technology leaders responsible for AI infrastructure and API spending who want to understand the economics behind production AI systems and establish better cost controls. Familiarity with LLM applications and basic Python will help readers get the most from the implementation-focused sections.

Build real-world DevOps skills through 90 days of hands-on projects. Learn Linux, Git, Terraform, Ansible, Docker, Kubernetes, and CI/CD to automate infrastructure, deploy applications, and launch your DevOps career with confidence

Key Features

• Learn DevOps by completing 90 days of structured, real-world projects
• Automate infrastructure and deployments using Terraform, Ansible, Docker, and Kubernetes
• Build production-ready CI/CD pipelines with Jenkins, GitHub Actions, and GitOps

Book Description

What you will learn

• Apply DevOps principles through hands-on, portfolio-ready projects
• Master Linux fundamentals and Git for collaborative workflows
• Provision infrastructure using Terraform and Infrastructure as Code
• Automate configuration with Ansible playbooks and roles
• Build, secure, and manage containerized apps with Docker
• Deploy and scale applications using Kubernetes
• Integrate databases and cloud services into real-world workflows
• Design and implement CI/CD pipelines using Jenkins, GitHub Actions, and ArgoCD

Who this book is for

This book is for system administrators, IT support engineers, and aspiring DevOps professionals who want to transition into automation-focused, cloud-native roles. If you're comfortable with the command line and managing servers but want to learn Infrastructure as Code, containers, Kubernetes, and CI/CD pipelines, this project-based guide will help you gain job-ready skills. No prior DevOps experience is required.

Et si la crise écologique se lisait d’abord sous nos pieds ?
Aucun autre élément de notre quotidien n’est aussi discret et silencieux que le sol. Longtemps considéré comme un simple support ou une ressource, il se retrouve aujourd’hui au cœur des débats sur l’artificialisation, la biodiversité et le climat. Mais que signifie réellement « consommer » le sol ? Et cette notion, est-elle spécifique à notre modernité industrielle ?
Cet ouvrage collectif, qui croise histoire, géographie, droit, urbanisme et sciences du vivant, explore les usages et les consommations du sol à travers le temps. Des sociétés préindustrielles aux politiques contemporaines de « zéro artificialisation nette », il éclaire les transformations des savoirs, des pratiques et des régulations, ainsi que les conflits de valeurs qui redéfinissent sans cesse la place du sol dans nos sociétés.
En replaçant les enjeux actuels dans une perspective historique, le livre dépasse les oppositions simplistes entre sols « naturels » et « artificiels » et propose une lecture critique des catégories qui orientent l’action publique. Il invite ainsi à repenser le sol comme un objet écologique, politique et social majeur, au cœur des équilibres territoriaux contemporains.

Progress from your first conversation to completing professional assignments, creating advanced AI workflows, and and enhancing your career.

Key Features

• Follow a structured visual learning path from simple chatbot to reliable professional AI partner.
• Learn through practical activities and complete projects instead of abstract explanations.
• Develop safe, responsible practices for reviewing AI output and protecting information.

Book Description

What you will learn

• Set up ChatGPT and become familiar with the interface
• Write clear prompts and refine responses through conversations
• Use web search, deep research, documents, images, and data
• Create professional documents, presentations, and visual reports
• Build tested, shareable skills and specialized custom GPTs
• Connect plugins, apps, and trusted MCP servers
• Design end-to-end workflows for complex assignments
• Use AI to advance your career and income

Who this book is for

This book is for professionals, entrepreneurs, freelancers, consultants, marketers, salespeople, writers, recruiters, analysts, managers, students, and career changers. It is suitable for complete beginners as well as existing ChatGPT users who want a more structured learning experience. No coding knowledge or previous AI experience is required.

Chaque année, les exploitants agricoles doivent faire face aux contrôles liés aux aides PAC. Ces contrôles, réalisés par différents organismes – DDT, DRAAF, ASP et DDPP – peuvent avoir des conséquences financières importantes en cas de non-conformité. Ce guide pratique vous accompagne pas à pas : Comprendre les règles de conditionnalité et les domaines de contrôle. Identifier et gérer les non-conformités pour éviter les pénalités. Suivre des check-lists claires pour chaque organisme (DDT, DRAAF, ASP, DDPP). Préparer votre posture lors des contrôles pour sécuriser vos démarches. Avec des annexes régionales sur les directives nitrates et des exemples concrets, ce guide est l’outil indispensable pour gagner du temps, sécuriser vos aides et maîtriser vos obligations réglementaires. Pratique, clair et ciblé, ce guide est conçu pour les professionnels de l’agriculture qui veulent être sûrs d’être en conformité

Description
Kubernetes is widely recognized as the de facto standard for orchestrating containerized applications in modern cloud-native environments. As organizations adopt microservices and scalable infrastructure, a deep understanding of Kubernetes is important for the team involved in application deployment, infrastructure automation, and cloud architecture. This book is designed to bridge the gap between basic container concepts and advanced, production-grade cloud-native engineering.

This book provides a comprehensive, hands-on approach to learning Kubernetes, beginning with core concepts of container orchestration. It explores the Kubernetes architecture, resource configurations, and advanced concepts like custom resources, security and access control, service meshes, and observability. Each chapter focuses on practical implementation, covering topics like designing scalable deployments, managing stateful applications, securing clusters, and building CI/CD pipelines with best practices that enable you to apply these concepts in production.

By the end of this book, you will have the skills to deploy, manage, and scale Kubernetes clusters across diverse environments. Whether you are a developer, DevOps engineer, or architect, you will gain a thorough and practical understanding of how to use Kubernetes effectively and efficiently at scale.

Description
This book forms the mathematical and logical foundation of modern computer science and plays a vital role in the design of intelligent and efficient computing systems. In today’s era of AI, compiler construction, cyber security, ML, data science, and advanced software engineering, understanding formal languages, automata, and computational complexity has become increasingly important in day-to-day life. The concepts of automata and computation theory are widely applied in algorithm design, programming language development, pattern recognition, NLP, and system optimization.

This book provides a comprehensive and systematic introduction to the fundamental concepts of computation. It begins with finite automata, regular expressions, and regular grammars, enabling readers to understand the basics of pattern recognition and language processing. It further explores context-free grammars and pushdown automata, which are essential for syntax analysis and compiler design. Advanced topics such as Turing machines, recursive and recursively enumerable languages, undecidability, Chomsky hierarchy, linear bounded automata, and computational complexity are discussed in a simple and structured manner.

By the end of this book, readers will gain a strong theoretical foundation in computation and develop the ability to analyze computational problems using formal methods. The book empowers you to analyze algorithms critically and solve complex computational problems in real-world computer science applications.

Build production-ready agentic AI systems that move beyond pilots with governed architecture, multi-agent orchestration, AgentOps, EvalOps, and Google Cloud.

Key Features

• Bridge the Day 2 production gap with secure, governed, cost-aware agent architecture patterns
• Engineer around the model with context, Harness Engineering, AgentOps, and EvalOps
• Scale human–agent delivery with the AI Squad Model beyond the Supervision Ceiling
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

What you will learn

• Distinguish proof of value from operable, governable agent systems
• Choose agent architectures while accounting for the Coordination Tax
• Design orchestration around the Three Cs for reliable outcomes
• Engineer governed context, memory, and harnesses for reuse at scale
• Define Agency Scope to expand autonomy deliberately and safely
• Separate AgentOps platform health from EvalOps decision quality
• Persist decisions, state, and evidence for audit and evaluation
• Scale AI Squads beyond the Supervision Ceiling with human accountability

Who this book is for

Enterprise architects, solution architects, technical directors, principal engineers, applied AI engineers, cloud consultants, AI platform teams, and engineering leaders who need to take agentic AI from pilots to governed production systems. A general understanding of modern software systems, cloud platforms, and APIs is expected; Python familiarity is helpful but not required.

Le vin quitte peu à peu la table gastronomique pour explorer de nouveaux territoires. Il réinvente son avenir face aux mutations de notre époque : réchauffement climatique, désaffection pour les rouges, quête de nature, de beauté, de simplicité et de convivialité. Aujourd’hui de nouveaux vins s’imposent. Ils sont plus légers ou plus personnalisés mais toujours surprenants et savoureux. Et c’est à l’apéritif que commence cette nouvelle aventure. Moment de partage par excellence, l’apéritif est universel… Mais en connaît-on vraiment l’histoire, les codes et les cultures qui l’entourent ? Sait-on qu’il offre une liberté unique pour découvrir tous les styles de vins, sans contrainte ? Comment ces vins sont-ils élaborés ? Pourquoi choisir l’un plutôt qu’un autre ? Quels sont ceux qu’il faut absolument découvrir aujourd’hui ? Comment les déguster au mieux ? À travers leurs réponses, les auteurs proposent une sélection actuelle et inspirante, éclairent les choix de l’amateur et dressent le portrait de vignerons qui incarnent ces vins d’avenir. Avec une approche à la fois sensible et éclairée, cet ouvrage vous emmène dans une véritable « école buissonnière du vin » : comprendre sans se compliquer, choisir sans se tromper et déguster sans complexe. Un guide libre, vivant et inspirant pour faire de CHAQUE APÉRO UNE EXPÉRIENCE

Prepare for the AIGP exam with a practical guide to AI governance, responsible AI, risk management, EU AI Act compliance, lifecycle controls, audits, trusted deployment, vendor oversight, and certification-ready concepts

Key Features

• Prepare for the AIGP exam with focused coverage of AI governance, risk, and responsible AI
• Apply EU AI Act, NIST AI RMF, ISO standards, audits, impact assessments, and lifecycle controls
• Use checklists and scenarios to govern generative AI, RAG, vendors, and model deployment

Book Description

What you will learn

• Explain AI systems, model types, lifecycle stages, and AIGP governance terminology
• Identify AI harms, affected parties, bias, privacy risk, security issues, and misuse
• Apply responsible AI principles through oversight, documentation, testing, and controls
• Build risk workflows using probability, severity, impact assessments, and risk tiering
• Interpret NIST AI RMF, OECD principles, ISO standards, and EU AI Act obligations
• Govern AI vendors, deployment, audits, drift risks, and prepare for the AIGP exam

Who this book is for

This book is for professionals preparing for the Artificial Intelligence Governance Professional (AIGP) certification and for those responsible for AI governance in practice. It is also useful for data scientists, AI engineers, business leaders, policy teams, consultants, and public sector professionals who must evaluate, approve, deploy, or monitor AI systems responsibly. No prior AI governance experience is required, although a basic understanding of AI, business operations, privacy, compliance, or technology projects will help readers apply the frameworks faster.

The Situated Science of Nicola Caputi examines the scientific writings of Nicola Caputi (1696–1761), a physician from Salento and member of the Neapolitan Academy of Sciences. The book offers the first comprehensive study of Caputi's oeuvre, reconstructing his role as a local observer within the early eighteenth-century scientific culture of the Kingdom of Naples.. At the center stands the De tarantulae anatome et morsu (1741), a natural history of the Apulian tarantula and tarantism that integrates anatomical dissections, microscopic observations, and clinical cases with reflections on testimony and authority. Alongside this major work, the volume analyzes Caputi's pharmacological dissertation on the delfinio—composed in 1733 and published in 1741—concerning a Salentine plant studied for its therapeutic efficacy against fevers, and his Guadina difesa (1751), an environmental inquiry into the salubrity of a contested body of water near Lecce.. Together, these writings illuminate the interplay between local knowledge, experimental practice, and epistemic self-representation, showing how wonders and singular phenomena could be investigated within the methodological framework of early modern natural philosophy.

Destiné aux étudiants dès le second cycle universitaire, aux enseignants, chercheurs et astronomes amateurs, cet ouvrage aborde la théorie la plus emblématique de la physique en combinant :
- une présentation complète mais didactique du formalisme actuel de la théorie ainsi que de ses prédictions : avance anormale du périhélie de Mercure, déviation de la lumière par un astre massif, redshift gravitationnel ;
- une analyse historique détaillée de la genèse de la théorie basée sur les articles écrits par Einstein pendant la période 1907-1918, agrémentée de nombreuses citations.
Il décrit les premières idées physiques d’Einstein (principe d’équivalence, influence du champ de gravitation sur la marche des horloges et la courbure des rayons lumineux) et montre toute la complexité de la genèse de la théorie dans le nouveau cadre de la géométrie riemanienne et du calcul différentiel absolu. Les différentes étapes de la genèse sont reprises en détail, de la théorie de l’Entwurf (1913) aux quatre articles de novembre 1915, ainsi que les premières applications de la théorie : étude des ondes gravitationnelles (1916, 1918) et premier modèle cosmologique (1917).
L’accent est mis sur la double stratégie physique et mathématique adoptée par Einstein pour trouver les équations de champ. Les erreurs et confusions qu’il a commises lors de cette genèse sont analysées et remises en contexte à l’aide de l’expertise des historiens des sciences.

Master the mindset, strategies, mechanisms, and systems needed to succeed in a principal engineering role

Key Features

• Define and communicate a technical vision that drives business outcomes
• Establish a high-performance engineering culture that scales
• Derisk and execute complex initiatives across teams and systems
• Expand your influence to drive organization-wide engineering impact

Book Description

What you will learn

• Identify bottlenecks and opportunities, then turn them into a business-aligned technical vision
• Establish scalable engineering standards and mechanisms
• Use clear writing and purposeful meetings to sharpen thinking and drive decisions
• Hire, develop, and grow high-agency engineers
• Set standards and workflows for GenAI and coding agents
• Derisk and execute complex, ambiguous initiatives
• Design scalable, resilient, and observable software systems
• Set meaningful goals and metrics tied to business impact

Who this book is for

This book is for senior and staff engineers preparing for principal roles and principal engineers seeking greater organizational impact. CTOs, VPs of Engineering, technical directors, and other senior engineering leaders will also find it useful for developing principal-level talent and stronger engineering cultures. Readers should have experience designing, building, and operating software systems; no specific language, platform, or technology stack is required.

Ce super aide-mémoire en couleurs présente sous une forme visuelle l'essentiel des principes et techniques de base de la chimie analytique  (méthodes séparatives, méthodes spectrométriques, méthodes couplées) à connaître en vue des examens et concours. Il s'appuie sur de nombreux exemples et applications tournés notamment vers les sciences de la vie. Plus de 500 schémas et photos regroupés par thème et sous la forme de fiches sont commentés pour réviser d'un seul coup d'oeil !  L’image a volontairement été privilégiée, le texte d’accompagnement résumant les idées essentielles. 

Go beyond simple prompts and build production-grade AI agents with LangChain and LangGraph, from ReAct reasoning loops to tool-calling, RAG, and context engineering.

Key Features

• Build and debug AI agents using LangChain, LangGraph, and LangSmith
• Implement ReAct reasoning loops, tool calling, and structured outputs
• Apply context engineering, MCP integration, and RAG to real-world agent workflows

Book Description

What you will learn

• Build AI agents with LangChain primitives, tools, and structured outputs
• Understand the agent loop: call the model, run tools, feed results back, repeat
• Build stateful agents with LangGraph and apply context engineering
• Trace and debug agent behavior with LangSmith
• Design reliable agent harnesses for long-horizon execution
• Secure and improve production AI agent systems with memory, persistence, and guardrails

Who this book is for

This book is for software developers and engineers, AI engineers, data scientists, researchers, and technical builders who want to understand how modern AI agents work and how to build them with LangChain and LangGraph. You should be comfortable with Python, Git, APIs, and basic debugging. No machine learning background is required, but the book assumes some programming experience and focuses on practical implementation rather than introductory coding.