Systems | Development | Analytics | API | Testing

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. But, we also know that experimentation alone doesn’t yield business value. Organizations need to usher their ML models out of the lab (i.e., the proof-of-concept phase) and into deployment, which is otherwise known as being “in production”.

Snowflake is the data backbone for thousands of businesses, enabling data access and governance needed to deliver value. Interactive use cases in some data applications and embedded analytics, however, pose a particular challenge. Traditionally, you needed an additional caching layer to provide the required speed and throughput these solutions require—which also increased costs and architectural complexity.

A look at Qlik Replicate's zero-footprint architecture and how it is designed for scalability and flexibility.