Systems | Development | Analytics | API | Testing

This guide will show you how to easily add Continual as the AI layer to your modern data stack with Snowflake at the core. The intention is to provide an introduction to using Continual on Snowflake. After completing this tutorial, users are invited to try more advanced examples. We are going to demonstrate connecting Continual to Snowflake, building feature sets and models from data stored in Snowflake, and analyzing and maintaining the predictive model continuously over time.

A global leader in pharmaceuticals found themselves faced with a unique spin on a common challenge: Their biopharmaceutical division — responsible for producing vaccines and generating over $1 billion in annual sales — was struggling to turn raw data into trusted insights. Data underlies everything the global pharmaceutical company does, however, without data they can trust, they would be at risk of taking longer to get vaccines to market and incurring higher expenses along the way.

Vaccine development became the top priority for the life sciences industry – delivering new vaccines at unprecedented speed and maneuvering large-scale production processes. Numerous factors helped accelerate the vaccine roll-out including prior research, genome sequencing, jumping the FDA approval queue and a plethora of testing volunteers. So now that we’ve experienced these advancements, how can the industry keep momentum to speed-up innovative solutions across healthcare?