Gitforgits feb 2026 (6 Ergebnisse)

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 164 pp. Englisch.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book practically brings you the easy-to-follow solutions wherein you can connect AI-driven automation to everyday business tools using Model Context Protocol (MCP), with Microsoft Excel and Word as the primary execution and delivery surfaces. This book focuses on practical integration patterns that developers and technical teams can apply directly in real business environments. You won't find any discussions of model internals or theoretical AI concepts here.The book presents a structured approach to building governed, auditable workflows. It shows how Excel rows act as requests, YAML playbooks define execution logic, and tools such as Jira, databases, document repositories, and reporting systems are integrated through well-defined contracts. We use Microsoft Word as a controlled output surface for reports and deliverables. There are audit logs and approval mechanisms in place to ensure transparency and traceability throughout the workflow lifecycle. I will guide you through designing MCP tool contracts, mapping Excel columns to tool arguments, normalizing results for analytics, and shaping outputs for Excel and Power BI. In this book, you will learn to implement safety patterns such as idempotency, retries, policy allowlists, tenant isolation, and least-privilege access. It includes practical chapters on connector development, authentication and secrets handling, governance controls, diagnostics, testing strategies, and deployment patterns for both scheduled and on-demand execution.I strongly recommend this book to developers, platform engineers, and technical architects who work with Microsoft 365. These professionals will learn to integrate AI-assisted workflows into existing business processes without unnecessary complexity.Key LearningsTurn Excel and Word into powerful AI-driven control and delivery hubs for business automation.Automate business workflows with AI Agents without exposing users, model internals and LLMs.Build predictable, auditable, and reusable automation using tool contracts and playbooks.Trigger real business actions across Jira, databases, and documents using Excel and Word.Make use of YAML playbooks to transform multi-step business processes into workflows.Make use of audit logs, approvals, and correlation IDs to execute transparent and compliant AI actions.Prevent duplicate actions using idempotency and request tracking.Practice versioning, policies, and strict access controls for scaling MCP connectors.Table of ContentMCP and Agent PrimerMicrosoft 365 Project BlueprintTool Contracts for Excel and Word AutomationBuilding MCP Connectors as Business ActionsAuthentication and Secrets for Microsoft 365 and SaaSGovernance, Approvals, and Audit TrailsWorkflow Runner with YAML PlaybooksJira Connector with Excel RequestsData and Knowledge ConnectorsShipping MCP Integrations across Microsoft 365 Teams.…

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -AI agents are getting easier to build, but the surrounding ecosystem of models, SDKs, and frameworks is changing quickly. A lot of agent apps get tricky to maintain since they depend too much on a certain provider, library, or deployment setup. This book looks at a practical alternative, which is to make AI agents whose main logic doesn't change while models, SDKs, and runtimes can be changed around it. It's not about using complicated frameworks. Rather, this book shows you simple architectural patterns that let you set up an agent application so that tools, schemas, prompts, and business logic can stay separate from the runtime layer.For starters, it'll be a simple loop with agents, and we'll gradually build on that with tools that make things deterministic, outputs in a structured JSON format, and schema validation. It'll teach skills, like switching between models through configuration, running the same agent with hosted models or local inference using vLLM, and isolating SDK-specific integrations behind small adapter layers. Later, we will focus on packaging and deployment, in which we will convert the agent into a command-line tool, expose it through a minimal HTTP API, and package the application using Docker. Ultimately, the book puts the project together as a reusable starter template that can be used as a basis for future agent-based applications.Instead of talking about shortcuts or automation, this book focuses on practical development patterns for building maintainable AI agents. Basically, this book is perfect for Python developers, software engineers, and AI practitioners who want a step-by-step process for designing agents that can adapt as the surrounding ecosystem changes.Key LearningsBuild GenAI agents using simple agent loop that accepts prompts, calls tools, and returns structured AI responses.Use structured JSON outputs and Pydantic schemas to make AI agent responses reliable and safe for automation.Design AI tools as deterministic Python functions so agents can call calculators, summarizers, and utilities predictably.Create portable AI agents by separating business logic from LLM and model APIs.Implement a model gateway pattern to switch between OpenAI models, local LLMs, or other providers via configuration.Run the same agent with OpenAI models or local LLM inference using vLLM.Prevent SDK lock-in by isolating AI SDK integrations behind runtime adapters.Use LLM regression prompts and schema validation for better stability of AI Agents during switching the models.Package AI agent as CLI tool and HTTP API for real applications and integrations.Deploy AI agents with Docker containers and environment variable.Table of ContentShipping GenAI Agent in MinutesBuilding Agent WorkflowsReliable and Structured Agent OutputSwitching Models without Rewriting AgentRunning vLLMDesigning Stable Business Logic across Multiple SDKsAgent Packaging and Deployment 122 pp. Englisch.…

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -For those who want to build controlled, reproducible AI systems entirely within your own infrastructure, this book is the most practical and implementation-focused trainer. Instead of relying on external APIs or cloud-hosted intelligence services, this book clearly demonstrates how Apache Spark can orchestrate data preparation, model training, batch inference, reporting, and LLM acceleration in a disciplined and transparent way.As the book opens, it swiftly defines private AI, making it clear that external AI calls are not allowed, full ownership of datasets and model assets is imperative, and repeatable runs with traceable outputs are essential. I will use a realistic sample to show you how to build an end-to-end workflow that ingests raw data, normalizes it into a stable schema, trains a baseline classifier, extracts keywords, generates summaries, and produces structured reports. There's no doubt that each step is implemented with clarity and attention to maintainability. You can be sure that logging, manifests, and monitoring are embedded from the start. We implement classic machine learning techniques, vLLM, performance measurement, batch processing patterns, quarantine handling, and structured metrics to make private AI more usable and compete with cloud-based AI.Beyond experimentation, the book transitions seamlessly into packaging and routine execution. It will teach you to bundle multiple stages into a single command workflow, schedule daily or weekly runs, generate compact run reports, and adapt the architecture to new datasets without redesigning the system. It does not promise instant transformation or one-click AI solutions. Instead, it provides a structured path to building a sustainable private AI backbone using Spark as the orchestration layer.Key LearningsNo external AI calls and full control over data, models, and repeatable runs.Stable canonical schema with downstream ML and reusable reporting.Infuse Classic ML with Spark without introducing LLM complexity.Carry out extractive summaries without hallucination risk.Complete traceability through manifests, prompt versions, and run logs.Implement data and batch flow, along with fast inference using vLLM.Extract inspectable data and surface out the hidden errors using quarantine tables.Measure and store performance for every run with stakeholder reporting.Design single-command pipeline with clear configs to build repeatable AI.Table of ContentUp and Running with Private AIData Workflows using Spark DataFramesPowerful NLP without LLMBatch Inference and Practical OutputsSmart SummariesBoosting with vLLM IntegrationPackaging Private AILibri GmbH, Europaallee 1, 36244 Bad Hersfeld 164 pp. Englisch.…

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book practically brings you the easy-to-follow solutions wherein you can connect AI-driven automation to everyday business tools using Model Context Protocol (MCP), with Microsoft Excel and Word as the primary execution and delivery surfaces. This book focuses on practical integration patterns that developers and technical teams can apply directly in real business environments. You won't find any discussions of model internals or theoretical AI concepts here.The book presents a structured approach to building governed, auditable workflows. It shows how Excel rows act as requests, YAML playbooks define execution logic, and tools such as Jira, databases, document repositories, and reporting systems are integrated through well-defined contracts. We use Microsoft Word as a controlled output surface for reports and deliverables. There are audit logs and approval mechanisms in place to ensure transparency and traceability throughout the workflow lifecycle. I will guide you through designing MCP tool contracts, mapping Excel columns to tool arguments, normalizing results for analytics, and shaping outputs for Excel and Power BI. In this book, you will learn to implement safety patterns such as idempotency, retries, policy allowlists, tenant isolation, and least-privilege access. It includes practical chapters on connector development, authentication and secrets handling, governance controls, diagnostics, testing strategies, and deployment patterns for both scheduled and on-demand execution.I strongly recommend this book to developers, platform engineers, and technical architects who work with Microsoft 365. These professionals will learn to integrate AI-assisted workflows into existing business processes without unnecessary complexity.Key LearningsTurn Excel and Word into powerful AI-driven control and delivery hubs for business automation.Automate business workflows with AI Agents without exposing users, model internals and LLMs.Build predictable, auditable, and reusable automation using tool contracts and playbooks.Trigger real business actions across Jira, databases, and documents using Excel and Word.Make use of YAML playbooks to transform multi-step business processes into workflows.Make use of audit logs, approvals, and correlation IDs to execute transparent and compliant AI actions.Prevent duplicate actions using idempotency and request tracking.Practice versioning, policies, and strict access controls for scaling MCP connectors.Table of ContentMCP and Agent PrimerMicrosoft 365 Project BlueprintTool Contracts for Excel and Word AutomationBuilding MCP Connectors as Business ActionsAuthentication and Secrets for Microsoft 365 and SaaSGovernance, Approvals, and Audit TrailsWorkflow Runner with YAML PlaybooksJira Connector with Excel RequestsData and Knowledge ConnectorsShipping MCP Integrations across Microsoft 365 TeamsLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 174 pp. Englisch.…

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -AI agents are getting easier to build, but the surrounding ecosystem of models, SDKs, and frameworks is changing quickly. A lot of agent apps get tricky to maintain since they depend too much on a certain provider, library, or deployment setup. This book looks at a practical alternative, which is to make AI agents whose main logic doesn't change while models, SDKs, and runtimes can be changed around it. It's not about using complicated frameworks. Rather, this book shows you simple architectural patterns that let you set up an agent application so that tools, schemas, prompts, and business logic can stay separate from the runtime layer.For starters, it'll be a simple loop with agents, and we'll gradually build on that with tools that make things deterministic, outputs in a structured JSON format, and schema validation. It'll teach skills, like switching between models through configuration, running the same agent with hosted models or local inference using vLLM, and isolating SDK-specific integrations behind small adapter layers. Later, we will focus on packaging and deployment, in which we will convert the agent into a command-line tool, expose it through a minimal HTTP API, and package the application using Docker. Ultimately, the book puts the project together as a reusable starter template that can be used as a basis for future agent-based applications.Instead of talking about shortcuts or automation, this book focuses on practical development patterns for building maintainable AI agents. Basically, this book is perfect for Python developers, software engineers, and AI practitioners who want a step-by-step process for designing agents that can adapt as the surrounding ecosystem changes.Key LearningsBuild GenAI agents using simple agent loop that accepts prompts, calls tools, and returns structured AI responses.Use structured JSON outputs and Pydantic schemas to make AI agent responses reliable and safe for automation.Design AI tools as deterministic Python functions so agents can call calculators, summarizers, and utilities predictably.Create portable AI agents by separating business logic from LLM and model APIs.Implement a model gateway pattern to switch between OpenAI models, local LLMs, or other providers via configuration.Run the same agent with OpenAI models or local LLM inference using vLLM.Prevent SDK lock-in by isolating AI SDK integrations behind runtime adapters.Use LLM regression prompts and schema validation for better stability of AI Agents during switching the models.Package AI agent as CLI tool and HTTP API for real applications and integrations.Deploy AI agents with Docker containers and environment variable.Table of ContentShipping GenAI Agent in MinutesBuilding Agent WorkflowsReliable and Structured Agent OutputSwitching Models without Rewriting AgentRunning vLLMDesigning Stable Business Logic across Multiple SDKsAgent Packaging and DeploymentLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 122 pp. Englisch.…