Best Practices for Architecting AI Systems,
Part 1: Design Principles

Intro – Setting the Table

Architecture is broadly defined as the carefully designed structure of ‘something’. Whether the ‘something’ in question is a building, software or even a restaurant menu, three central concepts must be understood and applied:

1. Design Principles – These lay the foundation for a creating successful Artificial Intelligence (AI) systems and ensuring their survival and evolution.
2. Scalability Considerations – As requirements grow, so do the challenges of maintaining performance and efficiency.
3. Deployment Strategies – Deploying AI systems requires careful planning and execution, especially when integrating with legacy systems.

This piece focuses on the first of those:

Design Principles

1. Agile Design Applied to Architecture: Agile design principles, found in software development, can significantly enhance the architectural process of designing AI systems. Agile methodologies prioritize flexibility, iterative progress and collaboration, which are important for the dynamic nature of AI projects.
2. Iterative Development: Constantly break down the AI project scope into smaller, manageable components (microservices). Develop and test these components iteratively, allowing for continuous improvement and adaptation based on feedback. This approach helps with implementation challenges discovered when testing architectures.
3. Cross-functional Teams: Assemble teams with diverse expertise, including data scientists, software engineers, testers, User Interface/User Experience (UI/UX) professionals and domain experts. This collaborative foundation ensures that the AI system is designed with a holistic view, considering various perspectives and requirements.

Continuous Integration and Delivery (CI/CD)

Continuous Integration and Delivery (CI/CD): Implement CI/CD pipelines to automate the testing and deployment of AI models and components. This approach accelerates the development process and ensures that updates are seamlessly integrated into the system.

1. Robust System Integration:
   AI systems often need to interact with existing third-party, Commercial Off-the-Shelf (COTS) or Government Off-the-Shelf (GOTS) software, databases and hardware. Ensuring robust system integration is fundamental for the seamless operation and scalability of AI solutions to utilize outside information and capabilities.
2. Modular Architecture: Design the AI system using a modular, service-oriented approach, where each component can function independently and interact with other components through well-defined interfaces. This modularity simplifies integration and allows for easier maintenance and scaling.
3. Standardized Protocols and Application Programming Interfaces (APIs): Use standardized communication protocols and APIs to facilitate interaction between the AI system and other software components. Standardization reduces compatibility issues and simplifies the integration process.
4. Data Management and Governance: Robust data management practices ensure the quality, consistency, and security of data used by any AI system. Implement strong data governance policies to manage data access, storage and processing, while ensuring compliance with regulatory requirements, which may differ by project and customer.

Personalized UI/UX

UI and UX are critical components of AI systems, as they directly impact user adoption and satisfaction. Personalizing the UI/UX can enhance the usability and effectiveness of AI solutions.

Here’s three ways to achieve personalized UI/UX:

1. User-Centered Design: Focus on the needs and preferences of end-users throughout the design process. Conduct user research, gather feedback and iterate on the design to create intuitive and user-friendly interfaces.
2. Adaptive Interfaces: Implement adaptive interfaces that can adjust based on user behavior and preferences. Use AI techniques, such as machine learning, to analyze user interactions and personalize the interface accordingly.
3. Accessibility and Inclusivity: Ensure that the AI system is accessible to all users, including those with disabilities. Design with inclusivity in mind, providing features like voice commands, screen readers and customizable interface options to cater to diverse user needs.

By designing your AI systems with intention, adhering to such fundamental design principles as agile methodologies, robust systems integration and personalized UI and UX, you can ensure you’ve laid the foundation for successful Artificial Intelligence (AI) systems that are resilient, ensuring their survival as needs expand and evolve.

Learn More

Jeff Jenkins, ManTech AI Systems Architect

To learn more about ManTech’s expertise in operationalizing Data and AI for federal agencies, contact Jeffrey Jenkins via AI@ManTech.com

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