Designing Artificial Intelligence Systems is a practical guide that helps understand how to create reliable, scalable, and manageable AI platforms. Instead of simply connecting a model, the book teaches how to build a full-fledged infrastructure necessary for the safe operation of AI applications in real-world conditions.
What This Book Offers
The publication reveals in detail how modern AI systems are structured and what components are necessary for their industrial operation. The author sequentially explains the key elements of architecture and shows how to combine them into a unified AI platform capable of supporting multiple applications and scenarios within an organization.
Main Topics and Features
Creating AI Platform Architecture
The reader lays the foundation of the system, starting from a universal interaction layer with various model providers to tools for the safe execution of actions.
- Support for multiple AI providers
- Flexible request routing system
- Infrastructure for scalability and reliability
Working with Memory and Context
The mechanisms of saving interaction history and constructing a long context are examined in detail.
- Approaches to long-term and operational memory
- Methods of storing and retrieving user data
- Using context in complex multi-step tasks
Integration with Corporate Data
It shows how to connect internal documents, databases, and company domain knowledge to the AI system.
- Working with corporate storage
- Organizing secure data access
- Document processing and knowledge extraction
Safe Use of Tools
Special attention is paid to managing the actions that the model can perform, as well as organizing safe and controlled procedures.
- Security policies and rules for executing actions
- Tools for code execution and API calls
- Mechanisms for preventing errors and abuses
Practical Work with Code
Each chapter is equipped with complete Python examples that can be adapted to individual tasks. The code forms working reference modules, from which a complete AI platform is assembled.
- Examples of services and interfaces
- Interaction and orchestration patterns
- Tools for quality control and monitoring
Operation and Industrial Readiness
Monitoring and Cost Control
Methods for evaluating query costs, analyzing load, and optimizing model usage are examined.
Assessing System Performance
It shows how to measure the performance of AI applications, track errors, and ensure stability as load increases.
Who This Book Is For
The material is targeted at software engineers, platform specialists, and technical architects involved in creating and scaling AI systems. Requires confident knowledge of Python and an understanding of basic software architecture principles.
