Best Practices for Architecting AI Systems,
Part 2: Scalability Considerations

Intro – Setting the Table

When architecting AI systems, three central concepts must be understood and applied:

1. Design Principles – These lay the foundation for 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 second of those (see also Part 1 – Design Principles):

Scalability Considerations

Without addressing scalability from the start, AI systems may be unable to grow. Teams should therefore consider horizontal and vertical scalability considerations from day one and prepare for the heaviest use cases to gracefully accommodate growth.

Training Requirements

Training AI models, especially deep learning models, is a resource-intensive process that requires careful planning and robust infrastructure. Architecting for training
requirements involves several key considerations:

1. Compute Power: Training AI models necessitates substantial computational power, often involving Graphics Processing Units (GPUs) or specialized
   hardware like Tensor Processing Units (TPUs). It's essential to architect a system that can efficiently distribute and parallelize training tasks to maximize resource
   utilization. Cloud platforms like Amazon Web Services, Google Cloud, and Microsoft Azure offer scalable solutions, enabling dynamic allocation of resources
   based on training needs.
2. Data Management: Effective data management is crucial for successful training. This includes ensuring data quality, preprocessing, and augmentation.
   Architecting a pipeline that can handle large volumes of training data, performing necessary transformations, and feeding the data into the model efficiently. Leveraging distributed storage systems and data lakes can help manage vast datasets seamlessly.
3. Experimentation and Versioning: AI development is iterative, requiring continuous experimentation and model versioning. Architecting a system that supports easy experimentation, tracking of model versions, and reproducibility of results is vital.

Large Scales of Data at Runtime

Once an AI model is trained, it must be deployed to assess its ability to handle real-time data processing and inference. Architecting for runtime data handling involves
addressing several challenges:

1. Scalability and Performance: The system must be designed to scale horizontally, allowing it to handle increasing volumes of data without degrading
   performance. This can be achieved through microservices architecture, containerization (using Docker and Kubernetes), and load balancing. Ensuring low latency and high throughput is critical, especially for applications requiring real-time responses.
2. Data Ingestion and Processing: Architecting a robust data ingestion pipeline is essential for processing large-scale data at runtime. This includes integrating
   with various data sources, performing real-time data transformations, and ensuring data consistency. Many open-source technologies can facilitate real- time data streaming and processing while enabling data scientists and engineers to add value.
3. Monitoring and Maintenance: Continuous monitoring and maintenance of the AI system is crucial to ensure its reliability and performance. Implementing observability tools can help monitor system health, track key performance metrics, and detect anomalies. Automated maintenance processes, such as rolling updates and auto-scaling, can further enhance system robustness.

Large Numbers of Users

AI systems often need to serve a large and diverse user base, necessitating an architecture that can handle high concurrency and ensure a seamless user experience:

1. Load Balancing and Traffic Management: To manage high user traffic, implementing effective load balancing strategies is essential. This involves distributing incoming requests across multiple servers to ensure even load distribution and prevent bottlenecks. On-premises and cloud-based load balancers can help manage traffic efficiently.
2. User Authentication and Authorization: Ensuring secure access to the AI system is paramount, especially when dealing with sensitive data. Architecting a robust authentication and authorization framework, using standards like Open Auth, JSON Web Tokens, and Security Assertion Markup Language, can help manage user access and protect against unauthorized use. Several open-source authentication management platforms can enable role and attribute-based access controls.
3. Personalization and User Experience: Delivering a personalized user experience will enhance user satisfaction and engagement. Architecting the system to collect and analyze user behavior data, and using this data to tailor responses and recommendations can create a more intuitive and effective user experience. Leveraging AI techniques like collaborative filtering and content- based filtering can aid in personalizing interactions.

Balancing performance and efficiency is key when designing AI systems, as is ensuring they are robust and flexible enough to scale effectively as needs 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

Or visit our site here.