Cannes: How ML saves us $1.7M a year on document previews
Recently, we translated the predictive power of machine learning (ML) into $1.7 million a year in infrastructure cost savings by optimizing how Dropbox generates and caches document previews. Machine learning at Dropbox already powers commonly-used features such as search, file and folder suggestion...
Hasnain says:
Pretty interesting case study of applying ML to an infrastructure problem, realizing some cost savings on a heavily used code path while maintaining the same user experience.
“Cannes is now deployed to almost all Dropbox traffic. As a result, we replaced an estimated $1.7 million in annual pre-warm costs with $9,000 in ML infrastructure per year (primarily from increased traffic to Suggest Backend and Predict Service).”
Posted on 2021-01-28T06:51:56+0000