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This topic covers building and deploying scalable applications using microservices architecture in the cloud. It explores Spring Boot, Spring Cloud, Docker, and Kubernetes for containerization and orchestration. Learn CI/CD pipelines and security best practices for cloud-native microservices development. Gain expertise in deploying resilient and scalable applications.
Learn to build scalable microservices using Java, Spring Boot, and cloud technologies like Kubernetes & GCP. Master Spring Cloud, Docker, security, and CI/CD.
Shabbir DawoodiLearn reactive programming with Spring WebFlux & Project Reactor. Build reactive microservices for scalable applications.
Pragmatic Code SchoolLLM features, RAG, agents and automation wired into a real product — built by the devs who shipped this site.
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Cloud Microservices enable faster AI model deployment and scaling by isolating components, improving resource utilization and reducing downtime. This architecture allows independent updates and experimentation, accelerating AI development cycles and fostering innovation.
Cloud Microservices facilitate machine learning workflows by breaking down complex tasks like data preprocessing, model training, and prediction into smaller, manageable services. This modularity enhances scalability and simplifies maintenance of AI pipelines.
AWS, Azure, and Google Cloud offer robust platforms for Cloud Microservices in AI, each providing managed services like container orchestration (Kubernetes) and serverless functions. Choose based on existing infrastructure and specific AI framework compatibility.
Developing Cloud Microservices for AI requires expertise in containerization (Docker), orchestration (Kubernetes), API design, and cloud platforms. Familiarity with programming languages like Python and experience with AI/ML frameworks are also crucial.
Cloud Microservices improve AI application resilience and scalability compared to monolithic architectures by allowing independent scaling of specific components. This approach also enables faster deployments and easier updates for individual AI services.