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ML infrastructure spans multiple environments — GPU clusters for training, edge devices for inference, cloud services for data processing — and services need to discover and communicate reliably across them. Hard-coded IP addresses, DNS-based service discovery, and manual configuration break as nodes scale up and down. Consul solves this with distributed service discovery, health checking, and configuration management that works across data centers and cloud regions. Services register themselves, clients discover them by name, health checks remove unhealthy endpoints, and configuration changes propagate automatically — enabling ML teams to manage distributed inference and data pipelines without manual networking.

INTEGRATION GUIDE

1. Register and discover model serving endpoints, feature stores, and embedding services by logical name across clusters 2. Implement health checks that automatically route inference traffic away from overloaded or failing model servers 3. Store and distribute configuration values for ML pipeline parameters without redeploying services 4. Secure service-to-service communication with TLS between training, inference, and data pipeline services 5. Enable multi-region service discovery for geo-distributed ML inference with automatic failover

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INTEGRATION GUIDE

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