Gaining access to compute resources and orchestrating workloads requires extensive experience in tooling that becomes a costly distraction for data scientists and ML engineers. Infrastructure bottlenecks reduce velocity and precision and increase model ops friction, time to market, and operational risk.
Collaboration among distributed research teams without a unified tool is a liability. Workflows that lack a standardized process and a unified hub lead to re-work and hinders the ability for data scientists to find, understand, build-on, and contribute to the various models in R+D and production.
Data scientists and machine learning engineers spend roughly 25% of their time developing models. This means that 75% of their time is spent on costly distractions related to tooling and infrastructure. A end-to-end ML pipeline enables a rapid model delivery cycle without the need to perform cumbersome routine tasks.
Experiment faster and deliver more breakthroughs. Remove DevOps pain with independent access to compute. Use the right tools for the job. Share and repurpose work across the team. More easily get models from development into production.
Create an organizational capability out of machine learning. Gain visibility into work happening across the organization. Accelerate the data machine learning management lifecycle. Reduce regulatory and operational risk while maintaining and updating models more frequently, with greater precision.
Support data science without sacrificing governance or security. Future-proof your machine learning stack. Gain transparency with complete model reproducibility. Offer a centralized platform for collaboration. Reduce model ops friction for rapid model delivery and iteration.