Harnessing Microservices for Scalable, Fault-Tolerant ML Systems

Learn how to implement microservices architecture for building scalable and fault-tolerant machine learning systems in this 21-minute conference talk from Conf42 ML 2025. Explore the limitations of monolithic architectures in ML environments and discover how microservices can address scalability, maintainability, and deployment challenges. Understand the key benefits of microservices including improved fault isolation, independent scaling, and technology flexibility, while also examining the complexities they introduce such as distributed system management and inter-service communication. Dive into communication patterns between microservices, covering synchronous and asynchronous approaches, API design principles, and message queuing systems. Examine various deployment strategies for ML microservices including containerization, orchestration platforms, and CI/CD pipelines. Master observability and monitoring techniques essential for distributed ML systems, including logging, metrics collection, distributed tracing, and performance monitoring. Discover emerging trends in the field such as serverless computing for ML, edge computing applications, and the integration of MLOps practices with microservices architecture.