Description
Snowflake and Databricks both aim to provide data science toolkits for machine learning workflows, albeit with different approaches and resources. While developing ML models is technically possible using either platform, the Hitachi Solutions Empower team tested which solution will be easier, faster, and cheaper to work with in terms of both user experience and business outcomes for our customers. To do this, we designed and conducted a series of experiments with use cases from the TPCx-AI benchmark standard. We developed both single-node and multi-node versions of these experiments, which sometimes required us to set up separate compute infrastructure outside of the platform, in the case of Snowflake. We also built datasets of various sizes (1GB, 10GB, and 100GB), to assess how each platform/node setup handles scale. Based on our findings, on the average, Databricks is faster, cheaper, and easier to use for developing machine learning models, and we use it exclusively for data science on the Empower platform. Snowflake’s reliance on third party resources for distributed training is a major drawback, and the need to use multiple compute environments to scale up training is complex…
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