Search results for "Mastering Prompt Engineering for Generative AI"
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Machine Learning and Systems Engineering
A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.
R 6,054.62
Machine Learning and Systems Engineering
A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.
R 6,055.52
Practical MCP Security
Learn to build and secure MCP servers for agentic AI systems by translating real-world threat models into OAuth 2.1, sandboxing, RBAC, and supply chain defenses that work in production.Key FeaturesThreat-model MCP systems across supply chain, runtime, and code-mode attack surfacesImplement OAuth 2.1, RBAC, tenant isolation, and policy-first authorisation for MCPHarden MCP servers with sandboxing, validation, monitoring, and supply chain controlsBook DescriptionAs agentic AI shifts from text generation to operational roles, it relies on the Model Context Protocol (MCP) to interface with databases and execute code. While MCP provides essential connectivity, it introduces a sophisticated attack surface. Practical MCP Security offers a hands-on framework for protecting these autonomous systems throughout their lifecycle.The book begins by deconstructing MCP architecture to establish a rigorous threat model, categorizing risks across supply chain integrity, runtime execution, and "code-execution" attack vectors. Readers will learn to map these vulnerabilities to testable security controls that mirror adversary behavior. It then details the technical implementation of OAuth 2.1 and scoped authorization, ensuring every interaction is authenticated and auditable.Beyond identity, the guide explores specialized threats like prompt injection, tool poisoning, and "rug pull" malicious updates. For enterprise production, it covers deployment hardening - including sandboxing, I/O validation, and secrets management - before addressing governance through RBAC, policies, and human-in-the-loop (HITL) mechanisms.Complete with Python implementations and verification checklists, this book provides the professional roadmap required to deploy agentic AI with institutional-grade security.What you will learnDesign and secure MCP servers for both local and remote agentic deploymentsDetect and mitigate agent-native attacks such as prompt injection and tool poisoningSandbox MCP tool execution using containers, gVisor, and Firecracker-style isolationSecure higher-risk MCP patterns, including remote execution and code-mode serversHarden the MCP supply chain using signing, verification, and dependency controlsEstablish monitoring, governance, and human-in-the-loop approval workflowsWho this book is forThis book is for software engineers, security engineers, platform architects, and DevSecOps practitioners who are building, deploying, or securing MCP-based agentic AI systems. It’s also useful for AI/ML engineers integrating third-party MCP servers, security teams assessing agentic AI risk, and engineering leaders defining governance and control requirements for tool-connected assistants. Familiarity with Python, REST APIs, and OAuth is helpful, but not required - core concepts and security patterns are introduced and explained as you go.
R 1,628.00
