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.
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