Meta has focused on enhancing reliability in Backend (BE) and Frontend (FE) networks for AI training, ensuring low latency and high throughput for GPUs and stable data flow for checkpointing. We’ve implemented a dual monitoring strategy using SLI and evidence-based collections for improved network health analysis and faster issue detection. Stricter controls, on-box agents, and robust SLOs for repair times have been adopted to enhance monitoring and quicken issue resolution. These measures maintain optimal network performance, which is crucial for large-scale training, demonstrating our commitment to a robust and reliable network infrastructure for advanced AI training.