Wydawca: Packt Publishing
Anderson Soares Furtado Oliveira, Elijah Low, Marcelo...
If you’re a web developer looking to leverage the power of AI in your projects, then this book is for you. Written by an AI and ML expert with more than 15 years of experience, AI Strategies for Web Development takes you on a transformative journey through the dynamic intersection of AI and web development, offering a hands-on learning experience.The first part of the book focuses on uncovering the profound impact of AI on web projects, exploring fundamental concepts, and navigating popular frameworks and tools. As you progress, you’ll learn how to build smart AI applications with design intelligence, personalized user journeys, and coding assistants. Later, you’ll explore how to future-proof your web development projects using advanced AI strategies and understand AI’s impact on jobs. Toward the end, you’ll immerse yourself in AI-augmented development, crafting intelligent web applications and navigating the ethical landscape.Packed with insights into next-gen development environments, AI-augmented practices, emerging realities, interfaces, and security governance, this web development book acts as your roadmap to staying ahead in the AI and web development domain.
AI Under Attack. A Practical Guide to Threats, Defenses, and Governance for AI Systems
Kris Kimmerle, David Okeyode
Contrary to general AI texts or cybersecurity books with limited AI coverage, this guide offers a comprehensive dive into securing the generative AI ecosystem.It moves through five parts: Foundations explains why AI security is unique, covering threat modeling, attack surfaces, and defense principles. Attacks examines vectors against system anatomy, data/models, prompt injection, memory, RAG, and agents, concluding with red teaming and evaluation. Designing, Deploying, and Architecting Secure AI Systems covers secure infrastructure/MLOps, APIs, defensive prompting, agent security, supply chain integrity, and Zero Trust patterns. Operationalizing AI Security and Responsibility addresses governance, risk, compliance (GRC), security operations, safety/alignment, and AI-driven misinformation. Building Sustainable AI Security Programs focuses on organizational capability, threat intelligence, collaboration, and the future of AI security. Throughout, you will gain access to practical insights and structured approaches applicable to real-world scenarios.By the end, you will be able to design, implement, and maintain security programs for generative AI, defend against advanced threats, communicate risks to stakeholders, and establish governance ensuring secure, compliant operations across the lifecycle.
Christoffer Noring, Anjali Jain, Marina Fernandez, Ayşe...
AI-Assisted Programming for Web and Machine Learning shows you how to build applications and machine learning models and automate repetitive tasks.Part 1 focuses on coding, from building a user interface to the backend. You’ll use prompts to create the appearance of an app using HTML, styling with CSS, adding behavior with JavaScript, and working with multiple viewports. Next, you’ll build a web API with Python and Flask and refactor the code to improve code readability. Part 1 ends with using GitHub Copilot to improve the maintainability and performance of existing code. Part 2 provides a prompting toolkit for data science from data checking (inspecting data and creating distribution graphs and correlation matrices) to building and optimizing a neural network. You’ll use different prompt strategies for data preprocessing, feature engineering, model selection, training, hyperparameter optimization, and model evaluation for various machine learning models and use cases. The book closes with chapters on advanced techniques on GitHub Copilot and software agents. There are tips on code generation, debugging, and troubleshooting code. You’ll see how simpler and AI-powered agents work and discover tool calling.
AI-Native LLM Security. Threats, defenses, and best practices for building safe and trustworthy AI
Vaibhav Malik, Ken Huang, Ads Dawson
Adversarial AI attacks present a unique set of security challenges, exploiting the very foundation of how AI learns. This book explores these threats in depth, equipping cybersecurity professionals with the tools needed to secure generative AI and LLM applications. Rather than skimming the surface of emerging risks, it focuses on practical strategies, industry standards, and recent research to build a robust defense framework.Structured around actionable insights, the chapters introduce a secure-by-design methodology, integrating threat modeling and MLSecOps practices to fortify AI systems. You’ll discover how to leverage established taxonomies from OWASP, NIST, and MITRE to identify and mitigate vulnerabilities. Through real-world examples, the book highlights best practices for incorporating security controls into AI development life cycles, covering key areas such as CI/CD, MLOps, and open-access LLMs.Built on the expertise of its co-authors—pioneers in the OWASP Top 10 for LLM applications—this guide also addresses the ethical implications of AI security, contributing to the broader conversation on trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI technologies with confidence and clarity.*Email sign-up and proof of purchase required
AI-Powered Commerce. Building the products and services of the future with Commerce.AI
Andy Pandharikar, Frederik Bussler
Commerce.AI is a suite of artificial intelligence (AI) tools, trained on over a trillion data points, to help businesses build next-gen products and services. If you want to be the best business on the block, using AI is a must.Developers and analysts working with AI will be able to put their knowledge to work with this practical guide. You'll begin by learning the core themes of new product and service innovation, including how to identify market opportunities, come up with ideas, and predict trends. With plenty of use cases as reference, you'll learn how to apply AI for innovation, both programmatically and with Commerce.AI. You'll also find out how to analyze product and service data with tools such as GPT-J, Python pandas, Prophet, and TextBlob. As you progress, you'll explore the evolution of commerce in AI, including how top businesses today are using AI. You'll learn how Commerce.AI merges machine learning, product expertise, and big data to help businesses make more accurate decisions. Finally, you'll use the Commerce.AI suite for product ideation and analyzing market trends.By the end of this artificial intelligence book, you'll be able to strategize new product opportunities by using AI, and also have an understanding of how to use Commerce.AI for product ideation, trend analysis, and predictions.
AI-Powered Commerce. Building the products and services of the future with Commerce.AI
Andy Pandharikar, Frederik Bussler
Commerce.AI is a suite of artificial intelligence (AI) tools, trained on over a trillion data points, to help businesses build next-gen products and services. If you want to be the best business on the block, using AI is a must.Developers and analysts working with AI will be able to put their knowledge to work with this practical guide. You'll begin by learning the core themes of new product and service innovation, including how to identify market opportunities, come up with ideas, and predict trends. With plenty of use cases as reference, you'll learn how to apply AI for innovation, both programmatically and with Commerce.AI. You'll also find out how to analyze product and service data with tools such as GPT-J, Python pandas, Prophet, and TextBlob. As you progress, you'll explore the evolution of commerce in AI, including how top businesses today are using AI. You'll learn how Commerce.AI merges machine learning, product expertise, and big data to help businesses make more accurate decisions. Finally, you'll use the Commerce.AI suite for product ideation and analyzing market trends.By the end of this artificial intelligence audiobook, you'll be able to strategize new product opportunities by using AI, and also have an understanding of how to use Commerce.AI for product ideation, trend analysis, and predictions.
AI-Powered DevOps with LLMs. Applying Large Language Models to Software Delivery and SRE
Gu Huangliang, Zheng Qingzheng, Niu Xiaoling, Che...
If you work in software engineering, DevOps, SRE, or platform teams, this book written by enterprise digital transformation specialists demonstrates how large language models (LLMs) can enhance automation, software delivery, and operational reliability across modern engineering organizations. To build familiarity, the book begins hands-on with the technical underpinnings of LLMs, including Transformers, GPT architectures, and fine-tuning techniques such as LoRA and QLoRA. It then develops these foundations to demonstrate how retrieval-augmented generation (RAG) and agent-based systems can be embedded into real enterprise workflows. Across development, testing, operations, security, and project management scenarios, you will see how LLMs enhance code generation, automate testing, improve log analysis and incident response, support root cause analysis, and assist in risk-based decision-making. By the end of the book, you will be able to move from isolated model experimentation to scalable enterprise practice, designing intelligent DevOps and SRE workflows that are efficient, reliable, and strategically aligned.
Abhijit Dey, Srinivasan Shanmuganathan, David Roldán Martínez
In today’s FinTech landscape, AI is no longer optional, it’s non-negotiable. And it’s not here to replace product managers, but to enhance their skills and amplify their impact. This book is a comprehensive guide for product managers and FinTech innovators looking to harness generative AI and LLMs in financial services. As the industry moves beyond traditional apps into AI-driven platforms, this book offers a practical blueprint for building trust and driving innovation in a rapidly evolving landscape shaped by generative AI.Unlike other FinTech books that lean towards theory, this guide provides actionable, real-world insights. It covers foundational AI-first principles, offers deep dives into key FinTech verticals through success-and-failure case studies, and explores platform scaling and agentic AI. Each chapter centers on real scenarios including a FinTech startup case study to illustrate frameworks and best practices. You’ll learn to integrate AI responsibly, navigate regulatory hurdles, and design customer-centric, data-driven financial products. You’ll equip yourself with the practical tools and templates to confidently innovate within regulatory boundaries.By the end, you’ll be ready to design and scale AI-driven financial products, positioning yourself as a forward-thinking product leader in the AI-first FinTech era.