Scale ML workflows
All the compute – none of the complexity
Launch a job into any target environment to dramatically scale ML training workloads from local machine to distributed compute. Easily access external environments, better GPUs and clusters to increase the speed and predictable scale of your ML workflow.
Bridge ML practitioners and MLOps
Remove org silos with one common interface. Practitioners get the compute they need with all the infrastructure complexity abstracted away, while MLOps maintains oversight and observability across the infrastructure environments they manage. Work collaboratively to scale up and out ML activities.
Easily run continuous integration and evaluation jobs
Drastically reduce your cycle time to deploy approved models to production inference environments. Evaluate your models more often and more thoroughly and continuously, with the ability to reproduce runs with just one click. Use Sweeps on Launch to easily tune knobs and change hyperparameters before re-running jobs.
Improved observability for ML Engineers
Increase visibility into how your ML infra budget is being used...or not being utilized. Rationalize your spend on significant infrastructure investments, and set better defaults to ensure optimal and efficient use of those resources.