Super-interesting summary of a recent paper. Currently, resource demand in the public cloud has fairly low variability and pricing and capacity planning are, as a result, fairly straightforward.
My read into this is that data science workloads are still a small overall percentage of total cloud workloads—large data science jobs, while having the potential to be absolutely massive, are typically batch-oriented, with resources spun up specifically to train a particular model. Will this ratio change in the future as data science workloads become ever-more-common? Will this change how cloud resources are delivered?