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Showing results for tags 'design patterns'.
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In the ever-evolving landscape of software architecture, the integration of artificial intelligence (AI) into microservices architecture is becoming increasingly pivotal. This approach offers modularity, scalability, and flexibility, crucial for the dynamic nature of AI applications. In this article, we'll explore 10 key microservice design patterns that are essential for AI development, delving into how they facilitate efficient, robust, and scalable AI solutions. 1. Model as a Service (MaaS) MaaS treats each AI model as an autonomous service. By exposing AI functionalities through REST or gRPC APIs, MaaS allows for independent scaling and updating of models. This pattern is particularly advantageous in managing multiple AI models, enabling continuous integration and deployment without disrupting the entire system. View the full article -
Building complex container-based architectures is not very different from programming in terms of applying design best practices and principles. The goal of this article is to present three popular extensibility architectural patterns from a developer's perspective using well-known programming principles. Let's start with the Single Responsibility Principle. According to R. Martin, "A class should have only one reason to change." But classes are abstractions used to simplify real-world problems and represent software components. Hence, a component should have only one reason to change over time. Software services and microservices in particular are also components (runtime components) and should have only one reason to change. Microservices are supposed to be a single deployable unit, meaning they are deployed independently of other components and can have as many instances as needed. View the full article
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