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Davis D: Amplified

Amplified

The Proven Path From AI Tools to Peak Productivity

- A comprehensive overview of the various fields of application of data science and artificial intelligence.
- Case studies from practice to make the described concepts tangible.
- Practical examples to help you carry out simple data analysis projects.
- NEW in the 3rd edition: Chapters on Vibe Coding and AI Agents

Data Science, Big Data, Artificial Intelligence and Generative AI are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them.
Using exercises and real-world examples, it will show you how to apply data science methods, build data platforms, and deploy data- and ML-driven projects to production. It will help you understand-and explain to various stakeholders-how to generate value from such endeavors. Along the way, it will bring essential data science concepts to life, including statistics, mathematics, and machine learning fundamentals, and explore crucial topics like critical thinking, legal and ethical considerations, and building high-performing data teams.
Readers of all levels of data familiarity-from aspiring data scientists to expert engineers to data leaders-will ultimately learn: how can an organization become more data-driven, what challenges might it face, and how can individuals help make that journey a success.

The Team of Authors consists of data professionals from business and academia, including data scientists, engineers, business leaders and legal experts. All are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and machine learning, and raising awareness of the opportunities and potential risks of these technologies. 

WHAT'S INSIDE //
- Critical Thinking and Data Culture: How evidence driven decision making is the base for effective AI.
- Machine Learning Fundamentals: Foundations of mathematics, statistics, and ML algorithms and architectures.
- Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, for real world applications.
- Foundation Models and Generative AI: Understand the strengths and challenges of generative models for text, images, video, and more.
- ML and AI in Production: Turning experimentation into a working data science product.
- AI Agents and Vibe Coding: Using AI in practice and creating new solutions.
- Presenting your Results: Essential presentation techniques for data scientists.

Generate Value from Data with Machine Learning and Data Analytics

The Handbook of Data Science and AI

In a world where AI is rapidly becoming embedded in everyday tools, this book helps you cut
through the noise to understand Microsoft’s growing Copilot ecosystem and confidently choose the
right tool for the right task.


You’ll start by untangling the different Copilot experiences in Microsoft 365. From there, you’ll explore
agents, learning why they matter and how they enable powerful automation without requiring deep
coding skills.


Along the way, you’ll get plain-language explanations of the buzzwords that make this space feel
overwhelming and a realistic view of what AI can and can’t do. By the end of the book, you won’t just
know the difference between a bot, a workflow, and an agent; you’ll know exactly when each is the right
choice and how to apply them with confidence.


What You will Learn:


• Choose the right Copilot or agent for specific business scenarios
• Understand how large language models work behind the scenes
• Interpret common AI terminology without the jargon
• Distinguish between bots, workflows, and agents and when to use each
• Identify strong AI use cases and avoid common pitfalls

 

Who This Book Is For

Business users, developers and Power Platform makers

Navigating Copilot and Agents in Microsoft 365

The Copilot Compass

How Small Businesses Use AI Automation to Save Time, Win More Customers & Get Paid Faster

Stop Doing It Manually

Artificial Intelligence is no longer a future concern—it’s a present-day disruptor. As vendors and partners increasingly adopt AI-enabled products and services, third-party and supply-chain risk professionals face a new challenge: managing a rapidly evolving risk landscape with limited guidance. This book delivers the clarity and structure needed to navigate that complexity.
Designed for business professionals—not just technologists—this practical guide walks readers through the full lifecycle of AI-related vendor risk, from intake to offboarding. With hands-on examples, actionable templates, and real-world use cases, it equips readers to assess and manage AI risk confidently, even in environments without dedicated IT security teams. It also explores how AI can be used within TPRM programs to enhance efficiency and accuracy.
As regulatory frameworks around AI continue to emerge and evolve, this book provides timely insight into compliance expectations and how they impact risk programs and leadership. Whether you're a seasoned risk manager or new to the field, you’ll find concise, jargon-free guidance that respects your time and delivers immediate value.
AI may be complex, but managing its risk doesn’t have to be. This book transforms confusion into clarity, helping you turn disruption into opportunity—and build a resilient, future-ready risk management program.
What You Will Learn:
How to measure risk and risk-based approaches.
Third-party risk frameworks.
How to assess the risk of AI with vendors.
Major AI risk management frameworks.
Regulatory guidance for AI--a country-by-country analysis.

Who This Book Is for:
· C-level suite: this is not designed to be overly technical but covers material enough to allow this level to be conversant in strategy and leadership needs to success.
· Director-level in Cyber and IT: this level of personnel are above the individual contributors (IC) and require the information in this book to translate the strategy goals set by C-suite and the tactics required for the ICs to implement and govern.
· GRC leaders and staff: the focus on governance in this book will assist these teams to better understand the strategy and technologies to determine the governance models needed. 
· Individual Contributors: although not designed to be a technical manual for engineering staff, it does provide a Rosetta Stone for them to understand how important strategy and governance are to

Solutions for Assessing and Managing Your AI Vendors and Systems

AI and Third-Party Risk

Animals

Der Praxisleitfaden für leistungsstarke KI-Agentensysteme

- Lernen Sie die zentralen Merkmale von KI-Agenten kennen und verstehen Sie, wie sie sich von klassischen Machine-Learning-Systemen unterscheiden

- Entdecken Sie die wichtigsten Komponenten und Designprinzipien von Agentensystemen

- Erfahren Sie, welche Tools und Frameworks sich für Entwicklung, Deployment und Absicherung von Agenten gegen Risiken eignen

- Entwerfen und implementieren Sie maßgeschneiderte KI-Lösungen, die die Effizienz und Innovation Ihrer Anwendungen steigern

Dieses Buch bietet einen klar strukturierten Rahmen, um robuste Agentensysteme zu entwickeln. Es zeigt, was moderne KI-Agenten ausmacht, welche Strategien für Planung, Ausführung und Tool-Nutzung funktionieren und wie Agenten durch Feintuning und Transfer Learning aus Erfahrung lernen. Das Buch liefert damit fundierte Antworten auf typische Fragestellungen aus der Praxis.

Michael Albada führt Sie in die Skalierung von Single- zu Multiagentensystemen ein und beleuchtet bewährte Muster wie demokratische, herarchische und Actor-Critic-Ansätze. Er vermittelt, wie man Agenten mit geeigneten Metriken bewertet, in der Produktion überwacht und sicher, ethisch und regelkonform betreibt. Prägnante Fallstudien aus Kundensupport, Recht, Werbung und Code-Review bieten das nötige Rüstzeug, um KI-Agentensysteme wirkungsvoll einzusetzen.

Entwurf, Implementierung, Monitoring

KI-Agenten entwickeln

The No-Code AI Agent Builder Series

A Beginner's Guide to Custom GPTs, Workflows & Automations

Build AI Agents Without Coding

Explore how autonomous AI agents are transforming financial markets and decision-making In Agentic Finance: Mathematics, Architecture, and Governance for AI Agents in Finance, Miquel Noguer Alonso and David Pacheco Aznar examine the emerging field where autonomous AI systems operate independently in financial contexts. The book explores how agentic technologies - AI systems capable of independent reasoning, planning, and action - are reshaping how financial institutions approach analysis, trading, and risk management. The book explores the mathematical foundations powering autonomous AI agents, from the algorithms enabling independent decision-making to the frameworks governing multi-agent financial systems. Readers gain insight into how innovators are applying these technologies across trading, portfolio management, and market analysis, along with the technical principles driving this transformation. Readers will also discover:&nbsp; <ul> <li>The core mathematical concepts underlying autonomous AI systems, including reinforcement learning, optimization, and probabilistic reasoning frameworks</li> <li>How leading innovators are deploying agentic AI across trading, risk assessment, portfolio optimization, and financial forecasting applications</li> <li>Technical architectures enabling AI agents to operate autonomously while maintaining appropriate oversight and governance in financial environments</li> <li>Real-world applications demonstrating how autonomous systems analyze markets, execute strategies, and adapt to changing conditions</li> <li>The evolving regulatory and ethical landscape surrounding autonomous AI decision-making in high-stakes financial contexts</li> </ul> Agentic Finance serves quantitative analysts, financial technologists, AI researchers, portfolio managers, and business leaders seeking to understand how autonomous AI systems are transforming financial services. It provides the technical foundation needed to evaluate and implement agentic technologies.

Mathematics, Architecture, and Governance for AI Agents in Finance

Agentic Finance

Professional and Applied Computing (R0)

Harshad Oak

Monish Darda

A New Era of Efficiency, Intelligent Design, and Democratization

The DeepSeek Moment

AI Identities introduces a groundbreaking concept: AI Agents should be recognized and governed as a new class of identity within enterprise environments. As artificial intelligence evolves from predictive models to autonomous agents with memory, goals, and tool access, enterprises face a new spectrum of identity risk that transcends traditional IAM frameworks. This book lays out the philosophical, architectural, and operational foundations necessary to govern these intelligent machine identities across their lifecycles.

Structured across six parts, the book begins by grounding readers in identity security and ownership. It then introduces the concept of AI agents as complex, evolving identities that demand governance, not just access control. It offers practical guidance on lifecycle management, trust, discovery, and incident response for AI agents, and concludes with future-facing perspectives on human-AI collaboration, critical infrastructure, and compliance.

This is not a coding manual or abstract ethics book—it’s a field guide for security professionals, architects, and digital leaders who must design, secure, and take responsibility for the AI identities acting on behalf of their organizations. The writing is crisp, deeply informed, and structured to support real-world decision-making in an era where the lines between automation and agency are quickly disappearing.

 

WHAT YOU WILL LEARN:

Understand the emerging category of AI Identities and how they differ from traditional machine accounts.

Develop strategies for ownership, governance, and lifecycle control of AI agents in enterprise environments.

Build a framework for discovery, trust scoring, explainability, and behavioral auditing of autonomous agents.

Learn how to respond to security incidents involving intelligent agents and prevent cascading failures.

Gain insights into the future of human-AI collaboration and the governance challenges posed by agentic AI.




WHO THIS BOOK IS FOR

This book is for enterprise security architects, identity professionals, risk officers, and technology executives responsible for the governance and security of digital systems. It is written to inform decision-makers and practitioners who need to understand how to integrate AI agents into their existing identity, compliance, and security programs.

Governing the Next Generation of Autonomous Actors

AI Identities

Artificial Intelligence is no longer a technology of the future-it is the competitive advantage of today.
In Dominate the Future with AI, Shiv Darshan Sharma provides a practical roadmap for entrepreneurs, professionals, freelancers, startups, and business leaders who want to harness AI to create wealth, accelerate growth, and build sustainable competitive advantages.
Through actionable frameworks, real-world case studies, AI business models, prompt engineering techniques, autonomous AI agents, monetization strategies, and step-by-step implementation plans, this book transforms complex AI concepts into practical business opportunities.
Inside this book, you will discover:
¿ How to identify profitable AI opportunities
¿ The essential AI skills required for future success
¿ Prompt engineering techniques that deliver results
¿ How to build and deploy AI-powered solutions
¿ AI monetization and wealth creation strategies
¿ Business transformation roadmaps using AI
¿ Career advancement strategies in the age of AI
¿ Real-world examples from startups, freelancers, and enterprises
Whether you are a business owner, working professional, consultant, student, or aspiring entrepreneur, this book will help you move beyond theory and start using AI as a powerful tool for growth, innovation, and financial success.
The future belongs to those who learn, adapt, and act. The time to dominate with AI is now.

Your Blueprint for Wealth, Work & Business Success

DOMINATE THE FUTURE WITH AI

A Guide for Humans, Engineers, and Enterprises

AI Powered Digital Twins

Architecting Intelligent Agents in Azure takes you from your first interaction with an AI agent to deploying a fully orchestrated, production-ready system that can reason, remember, collaborate, and act.
Using the Microsoft Agent Framework and Python, this book walks you through every engineering layer behind modern agentic systems, from grounding and memory to tools, semantic recall, and autonomous action.
Across the chapters, you'll build Thain, an Azure-native feedback agent that evolves into a multi-agent system with the ability to retrieve knowledge, coordinate tasks, and refine its performance.
With a balanced mix of architecture, hands-on code, and cloud patterns, you'll learn how Azure Cosmos DB, Azure AI Search, Azure Monitor, and serverless components come together to form intelligent, self-improving enterprise agents.
Whether you're an AI engineer, Azure developer, or solution architect, this book offers a practical, end-to-end guide to building agents that can reason, recall, collaborate, and grow over time.
What You Will Learn: 

<li>Build a fully functioning agent using Python and the Microsoft Agent Framework

</li><li>Implement reasoning loops, short-term memory, and tool-based actions

</li><li>Add persistent memory using Azure Cosmos DB and semantic recall using Azure AI Search

</li><li>Create safe and governed agents with telemetry, observability, and policy enforcement

</li><li>Integrate external systems through tools for tickets, documents, notifications, and workflows

</li><li>Orchestrate collaborative multi-agent systems with shared memory

</li><li>Deploy agentic workloads using Azure Functions, CI/CD pipelines, and cloud-native architecture

</li><li>Optimize cost, scale, and performance for enterprise production environments



 
Who This Book Is For: 
AI engineers and developers building real agentic systems and evaluating the Microsoft Agent Framework, vector search, and Azure AI services 
Cloud and Azure engineers looking to integrate AI capabilities deeply into existing or new applications 
Solution architects designing AI-native or AI-augmented enterprise platforms 
Full-stack engineers transitioning into AI enginee
</li>

Building Agentic Systems with Python and the Microsoft Agent Framework

Architecting Intelligent Agents in Azure

Amplified

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