Facebook's Expanding Machine Learning Infrastructure

Here at The Next Platform, we tend to keep a close eye on how the major hyperscalers evolve their infrastructure to support massive scale and evermore complex workloads. Not so long ago the core services were relatively standard transactions and operations, but with the addition of training and inferencing against complex deep learning models--something that requires a two-handed approach to hardware--the hyperscale hardware stack has had to quicken its step to keep pace with the new performance and efficiency demands of machine learning at scale. While not innovating on the custom hardware side quite the same way as Google, Facebook has shared some notable progress in fine-tuning its own datacenters. From its unique split network backbone, neural network-based viz system, to large-scale upgrades to its server farms and its work honing GPU use, there is plenty to focus on infrastructure-wise. For us, one of the more prescient developments from Facebook is its own server designs which now serve over 2 billion accounts as of the end of 2017, specifically its latest GPU-packed Open Compute based approach.