How To: Scaling a Machine Learning Model Using Pivotal Cloud Foundry

Scaling a model in response to user demand is crucial for bringing a machine learning model into production. In this blog post, we follow up on our previous post by showing how to scale this model in production using Pivotal Cloud Foundry (PCF). Pivotal Cloud Foundry makes it easy to scale an application using the command line interface (CLI) or the Apps Manager with no downtime. We utilize Apps Manager to horizontally scale out (spinning up new instances of our model) our application automatically utilizing PCF's load balancer, which reroutes new requests to appropriate instances of our model. Using the sentiment analysis analysis model we've built with Pivotal Greenplum and Python, we built a dashboard for analyzing live Tweets from the Twitter firehose.