An AI architecture is a plan that describes which technologies are used and how data flows in order to implement an AI solution within an organization. Depending on the use case, an AI architecture may be part of a larger data architecture that defines the overall framework that sets the standards for how data systems will interact with one another, such as how data are collected, stored, arranged, integrated, and accessed for use.
The AI architecture should be designed in the planning phase of an AI project and is most commonly developed by an AI architect, data engineer, or other high-level technical experts. When an organization does not have the appropriate technical capabilities internally, the work of developing a comprehensive AI architecture is sometimes outsourced to technology partners.
A well-developed architecture is a critical first step in the AI process because major data and technology decisions will be based on the plan. The plan should be customized for a clearly defined business use case so that its success can be measured against agreed-upon benchmarks. Further, the plan should be designed to adapt to future growth that aligns with business and budget projections.
Molecula is an operational AI company that enables businesses to deploy real-time analytics and AI through FeatureBase, its ultra low-latency database platform. FeatureBase is typically represented in both data and AI architectures. FeatureBase simplifies the flow and accessibility of data and is particularly beneficial with the development and productionization of models for ML and AI applications.
Molecula’s unique feature-first approach inverts the traditional AI architecture flow and allows the automated placement of feature extraction of all data at the beginning of the process. The impact of this architectural shift is that the once-manual process of making IT requests for big data projects can be automated and self-serve for development, analytics applications, and production. This reduces production delivery times from weeks or months to hours or days and makes the data reusable from project to project.
Learn More About AI Architecture
Towards Data Science: Fundamentals of Data Architecture
Wikipedia entry: Data architecture
Microsoft Azure Data Architecture Guide: Artificial intelligence architecture