Developer Blog: Training and Deploying Hundreds of Classifiers with Modal Labs – Taylor

At Taylor, we offer powerful off-the-shelf text classification models for complex taxonomies. For example, we help engineering teams at recruiting and HR companies categorize a job description into one of 1000 O*NET occupation codes. We have pre-trained models for topic classification, occupation classification, intent classification, and more. But for customers that want to use their own private taxonomy, we provide custom models, trained by us or through our self-serve training flow. Training and deploying custom models per-user presents several engineering challenges, including cold-starts, autoscaling, and long-running background processes. In this blog, I'll explain how we tackled these challenges using the best-in-class infrastructure provided by Modal Labs (opens in a new tab) . Modal is a serverless platform designed for AI and data-intensive workloads. It lets developers deploy a function to the cloud that auto-scales to meet demand, and scales to zero when not in use. Compare

At Taylor, we offer powerful off-the-shelf text classification models for complex taxonomies. For example, we help engineering teams at recruiting and HR companies categorize a job description into one of 1000 O*NET occupation codes. We have pre-trained models for topic classification, occupation classification, intent classification, and more. But for customers that want to use their own private taxonomy, we provide custom models, trained by us or through our self-serve training flow. Training and deploying custom models per-user presents several engineering challenges, including cold-starts,