From faster time-to-market to better fault tolerance and improved scalability, there are many reasons why organizations are turning away from monolithic applications and embracing microservices. When it comes to supporting edge and AI use cases, traditional microservices concepts must be adapted to meet the unique demands of these types of environments and workloads.
Building a microservices architecture that enables real-time, intelligent decision-making across our increasingly connected world requires several key technologies and practices:
- Designing with decentralized deployment in mind
- Leveraging containerization and orchestration to ensure lightweight, scalable, portable apps
- Deploying and scaling AI models on edge nodes
- Harnessing real-time data streaming and processing
- Adopting an event-driven approach
- Implementing comprehensive monitoring and observability