Facebook and Twitter have left most other companies around the world far behind when it comes to using machine learning to improve their business model. And while their practices haven’t always resulted in the best reactions from end-users, there’s much to be learned from these companies on what to do–and what not to do–when it comes to scaling and applying data analytics.
Get the Data You Need First
While Facebook seemingly uses machine learning for everything — it is used for content detection and content integrity, sentiment analysis, speech recognition, and fraudulent account detection, as well as operating functions like facial recognition, language translation, and content search functions. The Facebook algorithm manages all this while offloading some computation to edge devices in order to reduce latency.
