Scale Any Machine Learning Pipeline to Elastic Cloud Servers
Enabling elastic ML compute to run big data ETL, feature transformations, machine learning and deep learning pipelines with any R/Python/Matlab/C++ code.
Customer Benefits
100x
Up to 100x speed up for your machine learning pipeline
2h/d
Save 2 hours/day for each machine learning engineer
80%
Saving up to 80% on your cloud spending
Up to 100x speed up for your machine learning pipeline
Scaling any machine learning pipeline from a single server to an elastic group of 100 cloud instances to achieve 100x speed up.
Scale your favorite R/Python packages to thousands of CPUs across hundreds of machines. Never limited to Spark libraries anymore for large scale computation.
Typical use cases include hyper-parameter search, batch prediction, and feature transformation.
Save 2 hours/day for each machine learning engineer
Saving ML engineers’ time for configuring cloud infrastructure, monitoring cloud resource utilization and ML environment setup in each new cloud instance.
Let ML engineers easily create model reports from training logs.
Up to 80% saving on your cloud spending
Choosing the most cost-efficient hardware from cross-cloud including AWS/Azure/GCP.
Snark support Pre-emptible/Spot instances which are 70% cheaper than on-demand instances. Snark reschedules the jobs automatically for any spot interruption/instance pre-emption.