Scalable Machine Learning Training & Evaluation Platform
Design a distributed system for managing and executing machine learning training and evaluation jobs. The platform must support heterogeneous hardware requirements (CPUs and GPUs), handle long-running tasks (up to 24 hours), and provide real-time log streaming and artifact management. Key challenges include job scheduling, fault tolerance for worker failures, resource isolation, and handling high-volume log data at scale. Define the end-to-end flow from job submission to result retrieval, ensuring high reliability and observability.
PostgreSQLRedisKafkaS3KubernetesgRPCDockerFluentBit
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