CREDCost and speed
Scaling ML Model Inference through architectural separation
Our short summary of CRED’s engineering write-up. The full story, with all the detail, is on their blog.
Read the full write-up on engineering.cred.club (opens in a new tab)Problem
Inference latency and operational cost grew as more models and services were added.
Solution
They split model execution from request orchestration into separate Hosting and Serving layers, routing requests centrally to reduce hops and improve efficiency.
Want the architecture, code and numbers? They’re in the original post.
Read the full write-up on engineering.cred.club (opens in a new tab)