Architecting AI Software Systems : Crafting Robust and Scalable AI Systems for Modern Software Development.

Format:

Book

Author/Creator:

Avila, Richard D.

Contributor:

Ahmad, Imran.

Language:

English

Subjects (All):

Artificial intelligence.

Computer software--Development.

Computer software.

Physical Description:

1 online resource (212 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2025.

Summary:

Discover how to design intelligent software systems by balancing AI and traditional architecture.This guide offers a roadmap for robust, scalable AI-enabled systems, blending principles with practical insights.

Contents:

Cover

Title Page

Copyright Page

Forward

Contributors

Table of Contents

Preface

Part 1: Architecting Fundamentals

Chapter 1: Fundamentals of AI System Architecture

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Introduction to AI systems: architecting the future of intelligence

What is an AI system?

The pervasive impact of AI infrastructure: powering intelligent solutions across industries

Key components of AI system architectures

Microservice architectures: a modular approach to building complex AI systems

Advantages of microservices for AI

Challenges of microservice architectures

Real-world example: conversational AI microservices implementation

The four core microservices

Role of the API gateway

Conversation flow sequence

Key aspects of microservice communication

Implementation considerations for conversational AI microservices

Why microservices for conversational AI?

Considerations for an AI system

Scalability: handling growing data and model complexity

Performance: optimization techniques

Reliability: fault tolerance, error handling, and redundancy

Security: data privacy and model robustness

Data modeling: catalogs and ontologies

Modern AI deployment paradigms

Cloud-native AI architectures

Data lakes and data warehouses in AI architectures: foundations for data-driven intelligence

Data lakes: a vast reservoir of raw data

Data warehouses: structured repositories for analytics

The synergy of data lakes and data warehouses

AI on cloud computing: a game-changer for AI

Benefits of cloud-based AI

Major cloud AI platforms: accelerating innovation with comprehensive toolsets

Key cloud AI platforms

Summary

Relevant reading.

Chapter 2: The Case for Architecture

Consequences of architectural failures

The origins of architecting

The role of the architect

Balancing vision and precision in AI architecture

AI systems and architecture

The holder of the vision

The architectural cycle

Thinking like an architect

Maintaining architectural vision

Modern system architecting

Decision-making frameworks for AI architecture

Selecting the right AI approach

Multi-dimensional decision framework

Business alignment

Data science considerations

Technical constraints

Structured decision process

Balancing innovation and practicality

The language of software architecture

Governance and compliance considerations for AI systems

Governance framework for AI architectures

Explainability in AI architecture design

Regulatory compliance integration

Implementation considerations

Modeling and simulation

What is software systems modeling?

The role of modeling and simulation in AI/ML systems

Architecture and interfaces

Interfaces

Interfaces and AI

Relevant reading

Chapter 3: Software Engineering and Architecture

Understanding software complexity in AI systems

Integration complexity

Case study: healthcare AI integration

Functional complexity

Technical complexity

Verification complexity

Example: verification in computer vision

Human interface complexity

Architecting in practice

Approaches for taming software complexity

Developing the architecture

Integration and cohesion

Project management

Project initiation

Project planning

Project execution

Monitoring and control

Project closing

Case study: AI project management in action

Exercises

References

Part 2: Architecting AI Systems

Chapter 4: Conceptual Design for AI Systems.

Concept of Operations (CONOPS)

CONOPS for AI-centric systems

Understanding the current system

Data-centric view for AI systems

Non-functional requirements for AI systems

The business case for AI systems

Impact of AI technologies on business operations

Organizational integration and human impacts

Scenarios for AI-enabled systems

Creating effective scenarios

AI technology usage in scenarios

Defining success and constraints

Use cases for AI-enabled systems

Structure of effective use cases

User classes and AI interaction

Operational modes for AI-enabled systems

Configuration mode

Startup mode

Execution mode

Maintenance mode

Recovery mode

Shutdown mode

Risk mitigation through conceptual design

Data quality risk mitigation

Stakeholder expectation management

Integration risk mitigation

Case study: Retail recommendation system

CONOPS development

Business case

Scenarios and use cases

Operational modes

Implementation challenges and lessons learned

Chapter 5: Requirements and Architecture for AI Pipelines

Development pipelines

Data store requirements

Data volume and velocity

Data formats and processing approaches

Timeliness and technology selection

Non-functional requirements and governance

Support operations and specialized stores

Algorithmic development components

Data quality checks

Data transforms

Data summary

Model building, tuning, and verification

Configuration control

Machine learning performance

Computation infrastructure

Scale processing

Model tuning and verification

Code committal and DevOps

Production pipeline

Data stores

Data operations

Data cleansing

Data transformation

Model execution

Operational status monitoring

Model maintenance.

Results and end user stores

Pipeline operations store

Continuous development/integration

Architecture patterns and tactics

Non-functional requirements

Reliability

Maintainability

Usability

Chapter 6: Design, Integration, and Testing

Design fundamentals

Requirements

Performance requirements

Security requirements

Compliance requirements

Actors and use cases

System modes

Block definition diagrams

Machine learning model

Pipeline operations

Results store

System tactics and patterns

Key attributes

Maintainability tactics and patterns

Availability tactics and patterns

Essential patterns for AI systems

Integration and testing

Types of integrations

Integration harness

Testing types

Requirements testing

Use case and scenario testing

Load testing

Model prediction testing

Data quality testing

Error and fault recovery testing

Compliance testing

User interface testing

Continuous development and integration

Chapter 7: Architecting a Generative AI System - A Case Study

The business challenge: Knowledge management crisis

The vision: Transformation through generative AI

Aligning business and technical objectives

Data science objectives

The architecture: Core components and workflow

System overview

Key components

LLM: The cognitive engine

Retrieval system (vector database): The knowledge repository

Web search integration: Real-time information access

From static models to dynamic agents

LangChain agent workflow

User query input

Intelligent routing

Contextual augmentation

Web search (conditional)

Response generation

Feedback loop

Technical infrastructure.

Cloud compute architecture

End-to-end system architecture

Client tier: User access and experience

Presentation tier: Interface orchestration

Application tier: Business logic

Data tier: Information storage and retrieval

External services: Extending capabilities

User interaction patterns

Use case: Query resolution

Business impact

Operational transformation

Customer experience

Financial outcomes

Cultural evolution

Key architectural principles

Retrieval-Augmented Generation (RAG)

Adaptive query routing

Feedback-driven learning

Chapter 8: Insights and Future Directions

Architecture

Building AI-enabled systems

Data engineering

Data analytics and models

Conceptual design

Design, integration, and testing

Future directions of AI and architecture

Moving forward

Chapter 9: Unlock Your Book's Exclusive Benefits

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Description based on publisher supplied metadata and other sources.

ISBN:

1-80461-946-9

9781804619469

OCLC:

1543624798