Infrastructure

Scaling a Parallel Inference System with Asyncio

Scaling a Parallel Inference System with Asyncio

Machine learning (ML) is at the core of our strategy to accelerate drug discovery for novel cancer treatments, and scalable ML inference is at the core of our ability to rapidly analyze large molecular datasets. To scale up ML inference in a cost- and time-effective way, we built a parallel

Scaling Drug Development with Containerized Machine Learning

This blog post was originally published on the AWS Startups blog here. At Reverie Labs, we use computation to drive the development of therapeutics for cancer. To do this, we have built substantial cloud-based infrastructure to train machine learning models, deploy models to production, and build and ship internal-facing applications