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AI Engineering with Foundation Models: Systems, Patterns, and Practices
Paperback
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Publisher: Independently Published
Published: Nov 29 2025
Pages: 236
Weight: 0.92
Height: 0.50 Width: 7.00 Depth: 10.00
Language: English
Inside, you will explore how to integrate foundation models into real-world systems with clarity and confidence. You will learn how to design data pipelines, vector stores, retrieval layers, tool-based reasoning, and agentic coordination. You will see how to evaluate model quality, monitor behaviors, secure your system, and scale to production environments. You will also discover how to apply architectural patterns such as retrieval augmentation, planning and tool use, multi-agent collaboration, and continuous evaluation loops. Each chapter provides actionable insights, real-world examples, and engineering best practices that help you move from experimentation to reliable deployment.
Key topics include:
i. Foundation model capabilities and system roles
ii. Retrieval-augmented generation and knowledge integration
iii. Agents, orchestration flows, and tool interaction
iv. Data engineering, embeddings, and vector indexing
v. Testing, evaluation, guardrails, and responsible AI practices
vi. Deployment architectures and scaling strategies
vii. Monitoring, observability, and lifecycle management
If you want to build AI systems that are dependable, understandable, and ready for production, this book will guide you step by step. Equip yourself with the patterns and practices that modern engineering teams rely on. Start building high-impact AI systems with confidence.
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