Machine Learning at Facebook: Understanding Inference at the Edge
Meta (Israel) · Menlo School · +2 more institutions
Abstract
At Facebook, machine learning provides a wide range of capabilities that drive many aspects of user experience including ranking posts, content understanding, object detection and tracking for augmented and virtual reality, speech and text translations. While machine learning models are currently trained on customized data-center infrastructure, Facebook is working to bring machine learning inference to the edge. By doing so, user experience is improved with reduced latency (inference time) and becomes less dependent on network connectivity. Furthermore, this also enables many more applications of deep learning with important features only made available at the edge. This paper takes a data-driven approach to…
Citation impact
- FWCI
- 57.79
- Percentile
- 100%
- References
- 67
Authors
26Topics & keywords
- Computer science
- Inference
- Machine learning
- Enhanced Data Rates for GSM Evolution
- Artificial intelligence
- Ranking (information retrieval)
- Edge device
- Deep learning