Dive into these two technologies, understanding where they overlap, and where their strengths can be used together to achieve your microservices goals.
Using AI tools to help design, develop, modify, and deliver a microservice application requires the collaboration of stakeholders, SMEs, developers, and DevOps.
Learn how to build generic, easily configurable, testable reactive consumers, producers, and DLT with Kotlin, Spring Boot, WebFlux, and Testcontainers.
The article discusses the need for streaming data processing and evaluates available options. It explains that one size fits all is approach is not appropriate.
Learn about the design patterns of microservice software architecture to overcome challenges like loosely coupled services, defining databases, and more.
Explore event-driven data mesh architecture, and how when combined with AWS, it becomes a robust solution for addressing complex data management challenges.
Recent innovations like the Model Registry, ModelCars feature, and TrustyAI are delivering manageability, speed, and accountability for AI/ML workloads