Case Study: Merantix achieves flexible, cost-efficient machine learning operations with Looker

Empowering machine learning experts with Google Cloud managed services

Merantix, a machine learning company building AI solutions for businesses across industries, needed flexible, cost-efficient infrastructure to handle streaming data, archive ingestion, and temporary large-scale processing jobs. It also wanted accurate billing so it could test new ideas without being punished by long-running cloud costs. Merantix used Google Cloud products and managed services, including Cloud Dataflow and Cloud Bigtable, as part of its development workflow.

Google Cloud, through services such as Cloud Dataflow, helped Merantix spin up hundreds or thousands of machines for processing jobs, pay only for the time used, and eliminate the need for internal maintenance. According to the case study, this reduced bottlenecks for engineers, lowered costs for clients, and removed the 30% to 40% of engineering time previously spent maintaining Apache HBase. Google Cloud also improved performance and reduced downtime, while minute- or second-level billing made experimentation safer and cheaper.

Merantix

John McSpedon

Machine Intelligence Engineer