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ML Infrastructure Orchestration

ML Infrastructure Orchestration

In order for an End-to-End ML platform to be effective, it must have the ability to orchestrate the underlying compute and storage resources that will be required for both training and production. This could be in support of distributed training, where a number of machines may be spun up in order so that either a dataset or a model may be distributed across them. It could also be in support of scaling compute resources up and down to match the demand on the Production Inference API.  In short, it is critical that any ML platform be able to orchestrate that underlying infrastructure, regardless of its location. (See also Kubernetes Support)

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