“On average, our model deployment would take a quarter to complete. Today, that is 4x faster with Saturn Cloud.”
Daniel Salazar, Senior Data Scientist, Brigit
Deploying a new model could take up to a quarter, slowing innovation and preventing rapid response to user needs.
Data scientists relied heavily on central IT or DevOps to provision and modify environments, causing bottlenecks and delays.
Handling large-scale datasets for training and inference put a strain on traditional infrastructure and caused resource contention.
The team struggled with limited visibility into job runs (success rates, failure points, run durations), making it difficult to optimize processes and troubleshoot issues effectively.
Saturn Cloud provides pre-built, scalable computing environments.
Result The data science team deployed models in a matter of three weeks, significantly reducing time-to-market.
Each data scientist can create and customize environments without disrupting others, allowing them to focus on modeling rather than waiting for environment provisioning.
Result Streamlined experimentation and faster iteration, removing the reliance on centralized ops teams.
With the ability to provision clusters and manage resources on-demand, Saturn Cloud handles large datasets seamlessly.
Result Eliminates capacity concerns so the platform can grow with Brigit’s data needs.
Built-in scheduling and monitoring help the team manage job runs.
Result Clear insights into run history, successes, and failures—while highlighting future opportunities to enhance historical tracking.
Discover how Saturn Cloud can help your organization achieve rapid, independent model deployment—without sacrificing oversight or scalability.